traffic_light_fine_detector#
Purpose#
It is a package for traffic light detection using YoloX-s.
Training Information#
Pretrained Model#
The model is based on YOLOX and the pretrained model could be downloaded from here.
Training Data#
The model was fine-tuned on around 17,000 TIER IV internal images of Japanese traffic lights.
Trained Onnx model#
You can download the ONNX file using these instructions.
Please visit autoware-documentation for more information.
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 |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector |
~/expect/rois |
tier4_perception_msgs::msg::TrafficLightRoiArray |
The array of ROIs detected by map_based_detector without any offset |
Output#
| Name | Type | Description |
|---|---|---|
~/output/rois |
tier4_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 |
|---|---|---|---|
fine_detector_score_thresh |
double | 0.3 | If the objectness score is less than this value, the object is ignored |
fine_detector_nms_thresh |
double | 0.65 | IoU threshold to perform Non-Maximum Suppression |
Node Parameters#
| Name | Type | Default Value | Description |
|---|---|---|---|
data_path |
string | "$(env HOME)/autoware_data" | packages data and artifacts directory path |
fine_detector_model_path |
string | "" | The onnx file name for yolo model |
fine_detector_label_path |
string | "" | The label file with label names for detected objects written on it |
fine_detector_precision |
string | "fp32" | The inference mode: "fp32", "fp16" |
approximate_sync |
bool | false | Flag for whether to ues approximate sync policy |
gpu_id |
integer | 0 | ID for the selecting CUDA GPU device |
Assumptions / Known limits#
Reference repositories#
YOLOX github repository