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 MobileNetV2.
Totally 37600 (26300 for training, 6800 for evaluation and 4500 for test) TIER IV internal images of Japanese traffic lights were used for fine-tuning.
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 |
autoware_auto_perception_msgs::msg::TrafficLightRoiArray |
rois of traffic lights |
Output#
| Name | Type | Description |
|---|---|---|
~/output/traffic_signals |
autoware_auto_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 |
Core Parameters#
cnn_classifier#
| Name | Type | Description |
|---|---|---|
model_file_path |
str | path to the model file |
label_file_path |
str | path to the label file |
precision |
str | TensorRT precision, fp16 or int8 |
input_c |
str | the channel size of an input image |
input_h |
str | the height of an input image |
input_w |
str | the width of an input image |
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 |
Assumptions / Known limits#
(Optional) Error detection and handling#
(Optional) Performance characterization#
References/External links#
[1] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018, pp. 4510-4520, doi: 10.1109/CVPR.2018.00474.