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traffic_light_ssd_fine_detector#

Purpose#

It is a package for traffic light detection using MobileNetV2 and SSDLite.

Training Information#

Pretrained Model#

The model is based on pytorch-ssd and the pretrained model could be downloaded from here.

Training Data#

The model was fine-tuned on 1750 TierIV internal images of Japanese traffic lights.

Trained Onnx model#

Inner-workings / Algorithms#

Based on the camera image and the global ROI array detected by map_based_detection node, a CNN-based detection method enables highly accurate traffic light detection.

Inputs / Outputs#

Input#

Name Type Description
~/input/image sensor_msgs/Image The full size camera image
~/input/rois autoware_auto_perception_msgs::msg::TrafficLightRoiArray The array of ROIs detected by map_based_detector

Output#

Name Type Description
~/output/rois autoware_auto_perception_msgs::msg::TrafficLightRoiArray The detected accurate rois
~/debug/exe_time_ms tier4_debug_msgs::msg::Float32Stamped The time taken for inference

Parameters#

Core Parameters#

Name Type Default Value Description
score_thresh double 0.7 If the objectness score is less than this value, the object is ignored
mean std::vector [0.5,0.5,0.5] Average value of the normalized values of the image data used for training
std std::vector [0.5,0.5,0.5] Standard deviation of the normalized values of the image data used for training

Node Parameters#

Name Type Default Value Description
onnx_file string "./data/mb2-ssd-lite-tlr.onnx" The onnx file name for yolo model
label_file string "./data/voc_labels_tl.txt" The label file with label names for detected objects written on it
mode string "FP32" The inference mode: "FP32", "FP16", "INT8"
max_batch_size int 8 The size of the batch processed at one time by inference by TensorRT
approximate_sync bool false Flag for whether to ues approximate sync policy

Assumptions / Known limits#

Reference repositories#

pytorch-ssd github repository

MobileNetV2

  • 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.