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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.

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: TrafficLightDataStructure.jpg

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#

[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.

(Optional) Future extensions / Unimplemented parts#

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