Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
- Shunsuke Miura
Authors
traffic_light_classifier
Purpose
traffic_light_classifier is a package for classifying traffic light labels using cropped image around a traffic light. This package has two classifier models: cnn_classifier
and hsv_classifier
.
Inner-workings / Algorithms
cnn_classifier
Traffic light labels are classified by EfficientNet-b1 or MobileNet-v2.
Totally 83400 (58600 for training, 14800 for evaluation and 10000 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
The information of the models is listed here:
Name | Input Size | Test Accuracy |
---|---|---|
EfficientNet-b1 | 128 x 128 | 99.76% |
MobileNet-v2 | 224 x 224 | 99.81% |
hsv_classifier
Traffic light colors (green, yellow and red) are classified in HSV model.
About Label
The message type is designed to comply with the unified road signs proposed at the Vienna Convention. This idea has been also proposed in Autoware.Auto.
There are rules for naming labels that nodes receive. One traffic light is represented by the following character string separated by commas. color1-shape1, color2-shape2
.
For example, the simple red and red cross traffic light label must be expressed as “red-circle, red-cross”.
These colors and shapes are assigned to the message as follows:
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/image |
sensor_msgs::msg::Image |
input image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output
Name | Type | Description |
---|---|---|
~/output/traffic_signals |
tier4_perception_msgs::msg::TrafficSignalArray |
classified signals |
~/output/debug/image |
sensor_msgs::msg::Image |
image for debugging |
Parameters
Node Parameters
Name | Type | Description |
---|---|---|
classifier_type |
int | if the value is 1 , cnn_classifier is used |
data_path |
str | packages data and artifacts directory path |
Core Parameters
cnn_classifier
Name | Type | Description |
---|---|---|
classifier_label_path |
str | path to the model file |
classifier_model_path |
str | path to the label file |
classifier_precision |
str | TensorRT precision, fp16 or int8
|
classifier_mean |
vector<double> | 3-channel input image mean |
classifier_std |
vector<double> | 3-channel input image std |
apply_softmax |
bool | whether or not apply softmax |
hsv_classifier
Name | Type | Description |
---|---|---|
green_min_h |
int | the minimum hue of green color |
green_min_s |
int | the minimum saturation of green color |
green_min_v |
int | the minimum value (brightness) of green color |
green_max_h |
int | the maximum hue of green color |
green_max_s |
int | the maximum saturation of green color |
green_max_v |
int | the maximum value (brightness) of green color |
yellow_min_h |
int | the minimum hue of yellow color |
yellow_min_s |
int | the minimum saturation of yellow color |
yellow_min_v |
int | the minimum value (brightness) of yellow color |
yellow_max_h |
int | the maximum hue of yellow color |
yellow_max_s |
int | the maximum saturation of yellow color |
yellow_max_v |
int | the maximum value (brightness) of yellow color |
red_min_h |
int | the minimum hue of red color |
red_min_s |
int | the minimum saturation of red color |
red_min_v |
int | the minimum value (brightness) of red color |
red_max_h |
int | the maximum hue of red color |
red_max_s |
int | the maximum saturation of red color |
red_max_v |
int | the maximum value (brightness) of red color |
Training Traffic Light Classifier Model
Overview
This guide provides detailed instructions on training a traffic light classifier model using the mmlab/mmpretrain repository
File truncated at 100 lines see the full file
Package Dependencies
Deps | Name |
---|---|
autoware_cmake | |
ament_cmake_auto | |
cuda_utils | |
cv_bridge | |
image_transport | |
message_filters | |
rclcpp | |
rclcpp_components | |
sensor_msgs | |
tensorrt_classifier | |
tensorrt_common | |
tier4_perception_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/traffic_light_classifier.launch.xml
-
- input/image [default: ~/image_raw]
- input/rois [default: ~/rois]
- output/traffic_signals [default: classified/traffic_signals]
- data_path [default: $(env HOME)/autoware_data]
- classifier_label_path [default: $(var data_path)/traffic_light_classifier/lamp_labels.txt]
- classifier_model_path [default: $(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx]
- classifier_precision [default: fp16]
- use_gpu [default: true]
- classifier_type [default: 1]
- classifier_type [default: 0]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged traffic_light_classifier at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
- Shunsuke Miura
Authors
traffic_light_classifier
Purpose
traffic_light_classifier is a package for classifying traffic light labels using cropped image around a traffic light. This package has two classifier models: cnn_classifier
and hsv_classifier
.
Inner-workings / Algorithms
cnn_classifier
Traffic light labels are classified by EfficientNet-b1 or MobileNet-v2.
Totally 83400 (58600 for training, 14800 for evaluation and 10000 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
The information of the models is listed here:
Name | Input Size | Test Accuracy |
---|---|---|
EfficientNet-b1 | 128 x 128 | 99.76% |
MobileNet-v2 | 224 x 224 | 99.81% |
hsv_classifier
Traffic light colors (green, yellow and red) are classified in HSV model.
About Label
The message type is designed to comply with the unified road signs proposed at the Vienna Convention. This idea has been also proposed in Autoware.Auto.
There are rules for naming labels that nodes receive. One traffic light is represented by the following character string separated by commas. color1-shape1, color2-shape2
.
For example, the simple red and red cross traffic light label must be expressed as “red-circle, red-cross”.
These colors and shapes are assigned to the message as follows:
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/image |
sensor_msgs::msg::Image |
input image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output
Name | Type | Description |
---|---|---|
~/output/traffic_signals |
tier4_perception_msgs::msg::TrafficSignalArray |
classified signals |
~/output/debug/image |
sensor_msgs::msg::Image |
image for debugging |
Parameters
Node Parameters
Name | Type | Description |
---|---|---|
classifier_type |
int | if the value is 1 , cnn_classifier is used |
data_path |
str | packages data and artifacts directory path |
Core Parameters
cnn_classifier
Name | Type | Description |
---|---|---|
classifier_label_path |
str | path to the model file |
classifier_model_path |
str | path to the label file |
classifier_precision |
str | TensorRT precision, fp16 or int8
|
classifier_mean |
vector<double> | 3-channel input image mean |
classifier_std |
vector<double> | 3-channel input image std |
apply_softmax |
bool | whether or not apply softmax |
hsv_classifier
Name | Type | Description |
---|---|---|
green_min_h |
int | the minimum hue of green color |
green_min_s |
int | the minimum saturation of green color |
green_min_v |
int | the minimum value (brightness) of green color |
green_max_h |
int | the maximum hue of green color |
green_max_s |
int | the maximum saturation of green color |
green_max_v |
int | the maximum value (brightness) of green color |
yellow_min_h |
int | the minimum hue of yellow color |
yellow_min_s |
int | the minimum saturation of yellow color |
yellow_min_v |
int | the minimum value (brightness) of yellow color |
yellow_max_h |
int | the maximum hue of yellow color |
yellow_max_s |
int | the maximum saturation of yellow color |
yellow_max_v |
int | the maximum value (brightness) of yellow color |
red_min_h |
int | the minimum hue of red color |
red_min_s |
int | the minimum saturation of red color |
red_min_v |
int | the minimum value (brightness) of red color |
red_max_h |
int | the maximum hue of red color |
red_max_s |
int | the maximum saturation of red color |
red_max_v |
int | the maximum value (brightness) of red color |
Training Traffic Light Classifier Model
Overview
This guide provides detailed instructions on training a traffic light classifier model using the mmlab/mmpretrain repository
File truncated at 100 lines see the full file
Package Dependencies
Deps | Name |
---|---|
autoware_cmake | |
ament_cmake_auto | |
cuda_utils | |
cv_bridge | |
image_transport | |
message_filters | |
rclcpp | |
rclcpp_components | |
sensor_msgs | |
tensorrt_classifier | |
tensorrt_common | |
tier4_perception_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/traffic_light_classifier.launch.xml
-
- input/image [default: ~/image_raw]
- input/rois [default: ~/rois]
- output/traffic_signals [default: classified/traffic_signals]
- data_path [default: $(env HOME)/autoware_data]
- classifier_label_path [default: $(var data_path)/traffic_light_classifier/lamp_labels.txt]
- classifier_model_path [default: $(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx]
- classifier_precision [default: fp16]
- use_gpu [default: true]
- classifier_type [default: 1]
- classifier_type [default: 0]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged traffic_light_classifier at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
- Shunsuke Miura
Authors
traffic_light_classifier
Purpose
traffic_light_classifier is a package for classifying traffic light labels using cropped image around a traffic light. This package has two classifier models: cnn_classifier
and hsv_classifier
.
Inner-workings / Algorithms
cnn_classifier
Traffic light labels are classified by EfficientNet-b1 or MobileNet-v2.
Totally 83400 (58600 for training, 14800 for evaluation and 10000 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
The information of the models is listed here:
Name | Input Size | Test Accuracy |
---|---|---|
EfficientNet-b1 | 128 x 128 | 99.76% |
MobileNet-v2 | 224 x 224 | 99.81% |
hsv_classifier
Traffic light colors (green, yellow and red) are classified in HSV model.
About Label
The message type is designed to comply with the unified road signs proposed at the Vienna Convention. This idea has been also proposed in Autoware.Auto.
There are rules for naming labels that nodes receive. One traffic light is represented by the following character string separated by commas. color1-shape1, color2-shape2
.
For example, the simple red and red cross traffic light label must be expressed as “red-circle, red-cross”.
These colors and shapes are assigned to the message as follows:
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/image |
sensor_msgs::msg::Image |
input image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output
Name | Type | Description |
---|---|---|
~/output/traffic_signals |
tier4_perception_msgs::msg::TrafficSignalArray |
classified signals |
~/output/debug/image |
sensor_msgs::msg::Image |
image for debugging |
Parameters
Node Parameters
Name | Type | Description |
---|---|---|
classifier_type |
int | if the value is 1 , cnn_classifier is used |
data_path |
str | packages data and artifacts directory path |
Core Parameters
cnn_classifier
Name | Type | Description |
---|---|---|
classifier_label_path |
str | path to the model file |
classifier_model_path |
str | path to the label file |
classifier_precision |
str | TensorRT precision, fp16 or int8
|
classifier_mean |
vector<double> | 3-channel input image mean |
classifier_std |
vector<double> | 3-channel input image std |
apply_softmax |
bool | whether or not apply softmax |
hsv_classifier
Name | Type | Description |
---|---|---|
green_min_h |
int | the minimum hue of green color |
green_min_s |
int | the minimum saturation of green color |
green_min_v |
int | the minimum value (brightness) of green color |
green_max_h |
int | the maximum hue of green color |
green_max_s |
int | the maximum saturation of green color |
green_max_v |
int | the maximum value (brightness) of green color |
yellow_min_h |
int | the minimum hue of yellow color |
yellow_min_s |
int | the minimum saturation of yellow color |
yellow_min_v |
int | the minimum value (brightness) of yellow color |
yellow_max_h |
int | the maximum hue of yellow color |
yellow_max_s |
int | the maximum saturation of yellow color |
yellow_max_v |
int | the maximum value (brightness) of yellow color |
red_min_h |
int | the minimum hue of red color |
red_min_s |
int | the minimum saturation of red color |
red_min_v |
int | the minimum value (brightness) of red color |
red_max_h |
int | the maximum hue of red color |
red_max_s |
int | the maximum saturation of red color |
red_max_v |
int | the maximum value (brightness) of red color |
Training Traffic Light Classifier Model
Overview
This guide provides detailed instructions on training a traffic light classifier model using the mmlab/mmpretrain repository
File truncated at 100 lines see the full file
Package Dependencies
Deps | Name |
---|---|
autoware_cmake | |
ament_cmake_auto | |
cuda_utils | |
cv_bridge | |
image_transport | |
message_filters | |
rclcpp | |
rclcpp_components | |
sensor_msgs | |
tensorrt_classifier | |
tensorrt_common | |
tier4_perception_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/traffic_light_classifier.launch.xml
-
- input/image [default: ~/image_raw]
- input/rois [default: ~/rois]
- output/traffic_signals [default: classified/traffic_signals]
- data_path [default: $(env HOME)/autoware_data]
- classifier_label_path [default: $(var data_path)/traffic_light_classifier/lamp_labels.txt]
- classifier_model_path [default: $(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx]
- classifier_precision [default: fp16]
- use_gpu [default: true]
- classifier_type [default: 1]
- classifier_type [default: 0]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged traffic_light_classifier at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
- Shunsuke Miura
Authors
traffic_light_classifier
Purpose
traffic_light_classifier is a package for classifying traffic light labels using cropped image around a traffic light. This package has two classifier models: cnn_classifier
and hsv_classifier
.
Inner-workings / Algorithms
cnn_classifier
Traffic light labels are classified by EfficientNet-b1 or MobileNet-v2.
Totally 83400 (58600 for training, 14800 for evaluation and 10000 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
The information of the models is listed here:
Name | Input Size | Test Accuracy |
---|---|---|
EfficientNet-b1 | 128 x 128 | 99.76% |
MobileNet-v2 | 224 x 224 | 99.81% |
hsv_classifier
Traffic light colors (green, yellow and red) are classified in HSV model.
About Label
The message type is designed to comply with the unified road signs proposed at the Vienna Convention. This idea has been also proposed in Autoware.Auto.
There are rules for naming labels that nodes receive. One traffic light is represented by the following character string separated by commas. color1-shape1, color2-shape2
.
For example, the simple red and red cross traffic light label must be expressed as “red-circle, red-cross”.
These colors and shapes are assigned to the message as follows:
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/image |
sensor_msgs::msg::Image |
input image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output
Name | Type | Description |
---|---|---|
~/output/traffic_signals |
tier4_perception_msgs::msg::TrafficSignalArray |
classified signals |
~/output/debug/image |
sensor_msgs::msg::Image |
image for debugging |
Parameters
Node Parameters
Name | Type | Description |
---|---|---|
classifier_type |
int | if the value is 1 , cnn_classifier is used |
data_path |
str | packages data and artifacts directory path |
Core Parameters
cnn_classifier
Name | Type | Description |
---|---|---|
classifier_label_path |
str | path to the model file |
classifier_model_path |
str | path to the label file |
classifier_precision |
str | TensorRT precision, fp16 or int8
|
classifier_mean |
vector<double> | 3-channel input image mean |
classifier_std |
vector<double> | 3-channel input image std |
apply_softmax |
bool | whether or not apply softmax |
hsv_classifier
Name | Type | Description |
---|---|---|
green_min_h |
int | the minimum hue of green color |
green_min_s |
int | the minimum saturation of green color |
green_min_v |
int | the minimum value (brightness) of green color |
green_max_h |
int | the maximum hue of green color |
green_max_s |
int | the maximum saturation of green color |
green_max_v |
int | the maximum value (brightness) of green color |
yellow_min_h |
int | the minimum hue of yellow color |
yellow_min_s |
int | the minimum saturation of yellow color |
yellow_min_v |
int | the minimum value (brightness) of yellow color |
yellow_max_h |
int | the maximum hue of yellow color |
yellow_max_s |
int | the maximum saturation of yellow color |
yellow_max_v |
int | the maximum value (brightness) of yellow color |
red_min_h |
int | the minimum hue of red color |
red_min_s |
int | the minimum saturation of red color |
red_min_v |
int | the minimum value (brightness) of red color |
red_max_h |
int | the maximum hue of red color |
red_max_s |
int | the maximum saturation of red color |
red_max_v |
int | the maximum value (brightness) of red color |
Training Traffic Light Classifier Model
Overview
This guide provides detailed instructions on training a traffic light classifier model using the mmlab/mmpretrain repository
File truncated at 100 lines see the full file
Package Dependencies
Deps | Name |
---|---|
autoware_cmake | |
ament_cmake_auto | |
cuda_utils | |
cv_bridge | |
image_transport | |
message_filters | |
rclcpp | |
rclcpp_components | |
sensor_msgs | |
tensorrt_classifier | |
tensorrt_common | |
tier4_perception_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/traffic_light_classifier.launch.xml
-
- input/image [default: ~/image_raw]
- input/rois [default: ~/rois]
- output/traffic_signals [default: classified/traffic_signals]
- data_path [default: $(env HOME)/autoware_data]
- classifier_label_path [default: $(var data_path)/traffic_light_classifier/lamp_labels.txt]
- classifier_model_path [default: $(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx]
- classifier_precision [default: fp16]
- use_gpu [default: true]
- classifier_type [default: 1]
- classifier_type [default: 0]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged traffic_light_classifier at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
- Shunsuke Miura
Authors
traffic_light_classifier
Purpose
traffic_light_classifier is a package for classifying traffic light labels using cropped image around a traffic light. This package has two classifier models: cnn_classifier
and hsv_classifier
.
Inner-workings / Algorithms
cnn_classifier
Traffic light labels are classified by EfficientNet-b1 or MobileNet-v2.
Totally 83400 (58600 for training, 14800 for evaluation and 10000 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
The information of the models is listed here:
Name | Input Size | Test Accuracy |
---|---|---|
EfficientNet-b1 | 128 x 128 | 99.76% |
MobileNet-v2 | 224 x 224 | 99.81% |
hsv_classifier
Traffic light colors (green, yellow and red) are classified in HSV model.
About Label
The message type is designed to comply with the unified road signs proposed at the Vienna Convention. This idea has been also proposed in Autoware.Auto.
There are rules for naming labels that nodes receive. One traffic light is represented by the following character string separated by commas. color1-shape1, color2-shape2
.
For example, the simple red and red cross traffic light label must be expressed as “red-circle, red-cross”.
These colors and shapes are assigned to the message as follows:
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/image |
sensor_msgs::msg::Image |
input image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output
Name | Type | Description |
---|---|---|
~/output/traffic_signals |
tier4_perception_msgs::msg::TrafficSignalArray |
classified signals |
~/output/debug/image |
sensor_msgs::msg::Image |
image for debugging |
Parameters
Node Parameters
Name | Type | Description |
---|---|---|
classifier_type |
int | if the value is 1 , cnn_classifier is used |
data_path |
str | packages data and artifacts directory path |
Core Parameters
cnn_classifier
Name | Type | Description |
---|---|---|
classifier_label_path |
str | path to the model file |
classifier_model_path |
str | path to the label file |
classifier_precision |
str | TensorRT precision, fp16 or int8
|
classifier_mean |
vector<double> | 3-channel input image mean |
classifier_std |
vector<double> | 3-channel input image std |
apply_softmax |
bool | whether or not apply softmax |
hsv_classifier
Name | Type | Description |
---|---|---|
green_min_h |
int | the minimum hue of green color |
green_min_s |
int | the minimum saturation of green color |
green_min_v |
int | the minimum value (brightness) of green color |
green_max_h |
int | the maximum hue of green color |
green_max_s |
int | the maximum saturation of green color |
green_max_v |
int | the maximum value (brightness) of green color |
yellow_min_h |
int | the minimum hue of yellow color |
yellow_min_s |
int | the minimum saturation of yellow color |
yellow_min_v |
int | the minimum value (brightness) of yellow color |
yellow_max_h |
int | the maximum hue of yellow color |
yellow_max_s |
int | the maximum saturation of yellow color |
yellow_max_v |
int | the maximum value (brightness) of yellow color |
red_min_h |
int | the minimum hue of red color |
red_min_s |
int | the minimum saturation of red color |
red_min_v |
int | the minimum value (brightness) of red color |
red_max_h |
int | the maximum hue of red color |
red_max_s |
int | the maximum saturation of red color |
red_max_v |
int | the maximum value (brightness) of red color |
Training Traffic Light Classifier Model
Overview
This guide provides detailed instructions on training a traffic light classifier model using the mmlab/mmpretrain repository
File truncated at 100 lines see the full file
Package Dependencies
Deps | Name |
---|---|
autoware_cmake | |
ament_cmake_auto | |
cuda_utils | |
cv_bridge | |
image_transport | |
message_filters | |
rclcpp | |
rclcpp_components | |
sensor_msgs | |
tensorrt_classifier | |
tensorrt_common | |
tier4_perception_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/traffic_light_classifier.launch.xml
-
- input/image [default: ~/image_raw]
- input/rois [default: ~/rois]
- output/traffic_signals [default: classified/traffic_signals]
- data_path [default: $(env HOME)/autoware_data]
- classifier_label_path [default: $(var data_path)/traffic_light_classifier/lamp_labels.txt]
- classifier_model_path [default: $(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx]
- classifier_precision [default: fp16]
- use_gpu [default: true]
- classifier_type [default: 1]
- classifier_type [default: 0]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged traffic_light_classifier at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
- Shunsuke Miura
Authors
traffic_light_classifier
Purpose
traffic_light_classifier is a package for classifying traffic light labels using cropped image around a traffic light. This package has two classifier models: cnn_classifier
and hsv_classifier
.
Inner-workings / Algorithms
cnn_classifier
Traffic light labels are classified by EfficientNet-b1 or MobileNet-v2.
Totally 83400 (58600 for training, 14800 for evaluation and 10000 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
The information of the models is listed here:
Name | Input Size | Test Accuracy |
---|---|---|
EfficientNet-b1 | 128 x 128 | 99.76% |
MobileNet-v2 | 224 x 224 | 99.81% |
hsv_classifier
Traffic light colors (green, yellow and red) are classified in HSV model.
About Label
The message type is designed to comply with the unified road signs proposed at the Vienna Convention. This idea has been also proposed in Autoware.Auto.
There are rules for naming labels that nodes receive. One traffic light is represented by the following character string separated by commas. color1-shape1, color2-shape2
.
For example, the simple red and red cross traffic light label must be expressed as “red-circle, red-cross”.
These colors and shapes are assigned to the message as follows:
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/image |
sensor_msgs::msg::Image |
input image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output
Name | Type | Description |
---|---|---|
~/output/traffic_signals |
tier4_perception_msgs::msg::TrafficSignalArray |
classified signals |
~/output/debug/image |
sensor_msgs::msg::Image |
image for debugging |
Parameters
Node Parameters
Name | Type | Description |
---|---|---|
classifier_type |
int | if the value is 1 , cnn_classifier is used |
data_path |
str | packages data and artifacts directory path |
Core Parameters
cnn_classifier
Name | Type | Description |
---|---|---|
classifier_label_path |
str | path to the model file |
classifier_model_path |
str | path to the label file |
classifier_precision |
str | TensorRT precision, fp16 or int8
|
classifier_mean |
vector<double> | 3-channel input image mean |
classifier_std |
vector<double> | 3-channel input image std |
apply_softmax |
bool | whether or not apply softmax |
hsv_classifier
Name | Type | Description |
---|---|---|
green_min_h |
int | the minimum hue of green color |
green_min_s |
int | the minimum saturation of green color |
green_min_v |
int | the minimum value (brightness) of green color |
green_max_h |
int | the maximum hue of green color |
green_max_s |
int | the maximum saturation of green color |
green_max_v |
int | the maximum value (brightness) of green color |
yellow_min_h |
int | the minimum hue of yellow color |
yellow_min_s |
int | the minimum saturation of yellow color |
yellow_min_v |
int | the minimum value (brightness) of yellow color |
yellow_max_h |
int | the maximum hue of yellow color |
yellow_max_s |
int | the maximum saturation of yellow color |
yellow_max_v |
int | the maximum value (brightness) of yellow color |
red_min_h |
int | the minimum hue of red color |
red_min_s |
int | the minimum saturation of red color |
red_min_v |
int | the minimum value (brightness) of red color |
red_max_h |
int | the maximum hue of red color |
red_max_s |
int | the maximum saturation of red color |
red_max_v |
int | the maximum value (brightness) of red color |
Training Traffic Light Classifier Model
Overview
This guide provides detailed instructions on training a traffic light classifier model using the mmlab/mmpretrain repository
File truncated at 100 lines see the full file
Package Dependencies
Deps | Name |
---|---|
autoware_cmake | |
ament_cmake_auto | |
cuda_utils | |
cv_bridge | |
image_transport | |
message_filters | |
rclcpp | |
rclcpp_components | |
sensor_msgs | |
tensorrt_classifier | |
tensorrt_common | |
tier4_perception_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/traffic_light_classifier.launch.xml
-
- input/image [default: ~/image_raw]
- input/rois [default: ~/rois]
- output/traffic_signals [default: classified/traffic_signals]
- data_path [default: $(env HOME)/autoware_data]
- classifier_label_path [default: $(var data_path)/traffic_light_classifier/lamp_labels.txt]
- classifier_model_path [default: $(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx]
- classifier_precision [default: fp16]
- use_gpu [default: true]
- classifier_type [default: 1]
- classifier_type [default: 0]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged traffic_light_classifier at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
- Shunsuke Miura
Authors
traffic_light_classifier
Purpose
traffic_light_classifier is a package for classifying traffic light labels using cropped image around a traffic light. This package has two classifier models: cnn_classifier
and hsv_classifier
.
Inner-workings / Algorithms
cnn_classifier
Traffic light labels are classified by EfficientNet-b1 or MobileNet-v2.
Totally 83400 (58600 for training, 14800 for evaluation and 10000 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
The information of the models is listed here:
Name | Input Size | Test Accuracy |
---|---|---|
EfficientNet-b1 | 128 x 128 | 99.76% |
MobileNet-v2 | 224 x 224 | 99.81% |
hsv_classifier
Traffic light colors (green, yellow and red) are classified in HSV model.
About Label
The message type is designed to comply with the unified road signs proposed at the Vienna Convention. This idea has been also proposed in Autoware.Auto.
There are rules for naming labels that nodes receive. One traffic light is represented by the following character string separated by commas. color1-shape1, color2-shape2
.
For example, the simple red and red cross traffic light label must be expressed as “red-circle, red-cross”.
These colors and shapes are assigned to the message as follows:
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/image |
sensor_msgs::msg::Image |
input image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output
Name | Type | Description |
---|---|---|
~/output/traffic_signals |
tier4_perception_msgs::msg::TrafficSignalArray |
classified signals |
~/output/debug/image |
sensor_msgs::msg::Image |
image for debugging |
Parameters
Node Parameters
Name | Type | Description |
---|---|---|
classifier_type |
int | if the value is 1 , cnn_classifier is used |
data_path |
str | packages data and artifacts directory path |
Core Parameters
cnn_classifier
Name | Type | Description |
---|---|---|
classifier_label_path |
str | path to the model file |
classifier_model_path |
str | path to the label file |
classifier_precision |
str | TensorRT precision, fp16 or int8
|
classifier_mean |
vector<double> | 3-channel input image mean |
classifier_std |
vector<double> | 3-channel input image std |
apply_softmax |
bool | whether or not apply softmax |
hsv_classifier
Name | Type | Description |
---|---|---|
green_min_h |
int | the minimum hue of green color |
green_min_s |
int | the minimum saturation of green color |
green_min_v |
int | the minimum value (brightness) of green color |
green_max_h |
int | the maximum hue of green color |
green_max_s |
int | the maximum saturation of green color |
green_max_v |
int | the maximum value (brightness) of green color |
yellow_min_h |
int | the minimum hue of yellow color |
yellow_min_s |
int | the minimum saturation of yellow color |
yellow_min_v |
int | the minimum value (brightness) of yellow color |
yellow_max_h |
int | the maximum hue of yellow color |
yellow_max_s |
int | the maximum saturation of yellow color |
yellow_max_v |
int | the maximum value (brightness) of yellow color |
red_min_h |
int | the minimum hue of red color |
red_min_s |
int | the minimum saturation of red color |
red_min_v |
int | the minimum value (brightness) of red color |
red_max_h |
int | the maximum hue of red color |
red_max_s |
int | the maximum saturation of red color |
red_max_v |
int | the maximum value (brightness) of red color |
Training Traffic Light Classifier Model
Overview
This guide provides detailed instructions on training a traffic light classifier model using the mmlab/mmpretrain repository
File truncated at 100 lines see the full file
Package Dependencies
Deps | Name |
---|---|
autoware_cmake | |
ament_cmake_auto | |
cuda_utils | |
cv_bridge | |
image_transport | |
message_filters | |
rclcpp | |
rclcpp_components | |
sensor_msgs | |
tensorrt_classifier | |
tensorrt_common | |
tier4_perception_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/traffic_light_classifier.launch.xml
-
- input/image [default: ~/image_raw]
- input/rois [default: ~/rois]
- output/traffic_signals [default: classified/traffic_signals]
- data_path [default: $(env HOME)/autoware_data]
- classifier_label_path [default: $(var data_path)/traffic_light_classifier/lamp_labels.txt]
- classifier_model_path [default: $(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx]
- classifier_precision [default: fp16]
- use_gpu [default: true]
- classifier_type [default: 1]
- classifier_type [default: 0]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged traffic_light_classifier at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
- Shunsuke Miura
Authors
traffic_light_classifier
Purpose
traffic_light_classifier is a package for classifying traffic light labels using cropped image around a traffic light. This package has two classifier models: cnn_classifier
and hsv_classifier
.
Inner-workings / Algorithms
cnn_classifier
Traffic light labels are classified by EfficientNet-b1 or MobileNet-v2.
Totally 83400 (58600 for training, 14800 for evaluation and 10000 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
The information of the models is listed here:
Name | Input Size | Test Accuracy |
---|---|---|
EfficientNet-b1 | 128 x 128 | 99.76% |
MobileNet-v2 | 224 x 224 | 99.81% |
hsv_classifier
Traffic light colors (green, yellow and red) are classified in HSV model.
About Label
The message type is designed to comply with the unified road signs proposed at the Vienna Convention. This idea has been also proposed in Autoware.Auto.
There are rules for naming labels that nodes receive. One traffic light is represented by the following character string separated by commas. color1-shape1, color2-shape2
.
For example, the simple red and red cross traffic light label must be expressed as “red-circle, red-cross”.
These colors and shapes are assigned to the message as follows:
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/image |
sensor_msgs::msg::Image |
input image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output
Name | Type | Description |
---|---|---|
~/output/traffic_signals |
tier4_perception_msgs::msg::TrafficSignalArray |
classified signals |
~/output/debug/image |
sensor_msgs::msg::Image |
image for debugging |
Parameters
Node Parameters
Name | Type | Description |
---|---|---|
classifier_type |
int | if the value is 1 , cnn_classifier is used |
data_path |
str | packages data and artifacts directory path |
Core Parameters
cnn_classifier
Name | Type | Description |
---|---|---|
classifier_label_path |
str | path to the model file |
classifier_model_path |
str | path to the label file |
classifier_precision |
str | TensorRT precision, fp16 or int8
|
classifier_mean |
vector<double> | 3-channel input image mean |
classifier_std |
vector<double> | 3-channel input image std |
apply_softmax |
bool | whether or not apply softmax |
hsv_classifier
Name | Type | Description |
---|---|---|
green_min_h |
int | the minimum hue of green color |
green_min_s |
int | the minimum saturation of green color |
green_min_v |
int | the minimum value (brightness) of green color |
green_max_h |
int | the maximum hue of green color |
green_max_s |
int | the maximum saturation of green color |
green_max_v |
int | the maximum value (brightness) of green color |
yellow_min_h |
int | the minimum hue of yellow color |
yellow_min_s |
int | the minimum saturation of yellow color |
yellow_min_v |
int | the minimum value (brightness) of yellow color |
yellow_max_h |
int | the maximum hue of yellow color |
yellow_max_s |
int | the maximum saturation of yellow color |
yellow_max_v |
int | the maximum value (brightness) of yellow color |
red_min_h |
int | the minimum hue of red color |
red_min_s |
int | the minimum saturation of red color |
red_min_v |
int | the minimum value (brightness) of red color |
red_max_h |
int | the maximum hue of red color |
red_max_s |
int | the maximum saturation of red color |
red_max_v |
int | the maximum value (brightness) of red color |
Training Traffic Light Classifier Model
Overview
This guide provides detailed instructions on training a traffic light classifier model using the mmlab/mmpretrain repository
File truncated at 100 lines see the full file
Package Dependencies
Deps | Name |
---|---|
autoware_cmake | |
ament_cmake_auto | |
cuda_utils | |
cv_bridge | |
image_transport | |
message_filters | |
rclcpp | |
rclcpp_components | |
sensor_msgs | |
tensorrt_classifier | |
tensorrt_common | |
tier4_perception_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/traffic_light_classifier.launch.xml
-
- input/image [default: ~/image_raw]
- input/rois [default: ~/rois]
- output/traffic_signals [default: classified/traffic_signals]
- data_path [default: $(env HOME)/autoware_data]
- classifier_label_path [default: $(var data_path)/traffic_light_classifier/lamp_labels.txt]
- classifier_model_path [default: $(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx]
- classifier_precision [default: fp16]
- use_gpu [default: true]
- classifier_type [default: 1]
- classifier_type [default: 0]
- build_only [default: false]
Messages
Services
Plugins
Recent questions tagged traffic_light_classifier at Robotics Stack Exchange
Package Summary
Tags | No category tags. |
Version | 0.1.0 |
License | Apache License 2.0 |
Build type | AMENT_CMAKE |
Use | RECOMMENDED |
Repository Summary
Description | |
Checkout URI | https://github.com/ieiauto/autodrrt.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2025-05-30 |
Dev Status | UNKNOWN |
Released | UNRELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (-)
Good First Issues (-) Pull Requests to Review (-) |
Package Description
Additional Links
Maintainers
- Yukihiro Saito
- Shunsuke Miura
Authors
traffic_light_classifier
Purpose
traffic_light_classifier is a package for classifying traffic light labels using cropped image around a traffic light. This package has two classifier models: cnn_classifier
and hsv_classifier
.
Inner-workings / Algorithms
cnn_classifier
Traffic light labels are classified by EfficientNet-b1 or MobileNet-v2.
Totally 83400 (58600 for training, 14800 for evaluation and 10000 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
The information of the models is listed here:
Name | Input Size | Test Accuracy |
---|---|---|
EfficientNet-b1 | 128 x 128 | 99.76% |
MobileNet-v2 | 224 x 224 | 99.81% |
hsv_classifier
Traffic light colors (green, yellow and red) are classified in HSV model.
About Label
The message type is designed to comply with the unified road signs proposed at the Vienna Convention. This idea has been also proposed in Autoware.Auto.
There are rules for naming labels that nodes receive. One traffic light is represented by the following character string separated by commas. color1-shape1, color2-shape2
.
For example, the simple red and red cross traffic light label must be expressed as “red-circle, red-cross”.
These colors and shapes are assigned to the message as follows:
Inputs / Outputs
Input
Name | Type | Description |
---|---|---|
~/input/image |
sensor_msgs::msg::Image |
input image |
~/input/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output
Name | Type | Description |
---|---|---|
~/output/traffic_signals |
tier4_perception_msgs::msg::TrafficSignalArray |
classified signals |
~/output/debug/image |
sensor_msgs::msg::Image |
image for debugging |
Parameters
Node Parameters
Name | Type | Description |
---|---|---|
classifier_type |
int | if the value is 1 , cnn_classifier is used |
data_path |
str | packages data and artifacts directory path |
Core Parameters
cnn_classifier
Name | Type | Description |
---|---|---|
classifier_label_path |
str | path to the model file |
classifier_model_path |
str | path to the label file |
classifier_precision |
str | TensorRT precision, fp16 or int8
|
classifier_mean |
vector<double> | 3-channel input image mean |
classifier_std |
vector<double> | 3-channel input image std |
apply_softmax |
bool | whether or not apply softmax |
hsv_classifier
Name | Type | Description |
---|---|---|
green_min_h |
int | the minimum hue of green color |
green_min_s |
int | the minimum saturation of green color |
green_min_v |
int | the minimum value (brightness) of green color |
green_max_h |
int | the maximum hue of green color |
green_max_s |
int | the maximum saturation of green color |
green_max_v |
int | the maximum value (brightness) of green color |
yellow_min_h |
int | the minimum hue of yellow color |
yellow_min_s |
int | the minimum saturation of yellow color |
yellow_min_v |
int | the minimum value (brightness) of yellow color |
yellow_max_h |
int | the maximum hue of yellow color |
yellow_max_s |
int | the maximum saturation of yellow color |
yellow_max_v |
int | the maximum value (brightness) of yellow color |
red_min_h |
int | the minimum hue of red color |
red_min_s |
int | the minimum saturation of red color |
red_min_v |
int | the minimum value (brightness) of red color |
red_max_h |
int | the maximum hue of red color |
red_max_s |
int | the maximum saturation of red color |
red_max_v |
int | the maximum value (brightness) of red color |
Training Traffic Light Classifier Model
Overview
This guide provides detailed instructions on training a traffic light classifier model using the mmlab/mmpretrain repository
File truncated at 100 lines see the full file
Package Dependencies
Deps | Name |
---|---|
autoware_cmake | |
ament_cmake_auto | |
cuda_utils | |
cv_bridge | |
image_transport | |
message_filters | |
rclcpp | |
rclcpp_components | |
sensor_msgs | |
tensorrt_classifier | |
tensorrt_common | |
tier4_perception_msgs |
System Dependencies
Dependant Packages
Launch files
- launch/traffic_light_classifier.launch.xml
-
- input/image [default: ~/image_raw]
- input/rois [default: ~/rois]
- output/traffic_signals [default: classified/traffic_signals]
- data_path [default: $(env HOME)/autoware_data]
- classifier_label_path [default: $(var data_path)/traffic_light_classifier/lamp_labels.txt]
- classifier_model_path [default: $(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx]
- classifier_precision [default: fp16]
- use_gpu [default: true]
- classifier_type [default: 1]
- classifier_type [default: 0]
- build_only [default: false]