YOLOX-s Traffic Light Fine Detector for Autoware (traffic_light_fine_detector)
Camera-based traffic light detection models, used by the
autoware_traffic_light_fine_detector
node in
Autoware.
The models are YOLOX-s [1] detectors fine-tuned by TIER IV for traffic light detection. Given the full
camera image and the coarse ROIs predicted by the traffic_light_map_based_detector node, the fine detector
refines each ROI to a tight bounding box around the traffic light. The models are exported as ONNX; Autoware
builds the TensorRT engine from the ONNX file on first launch.
Model overview
| |
|---|
| Task | 2D traffic light detection (ROI refinement) in camera images |
| Architecture | YOLOX-s |
| Detected classes | traffic_light, pedestrian_traffic_light (plus a BACKGROUND label) |
| Runtime | TensorRT (FP16 by default, FP32 selectable) via the autoware_traffic_light_fine_detector ROS 2 node |
| Format | ONNX (Autoware builds the TensorRT engine locally on first launch) |
| License | Apache-2.0 |
Variants in this repository
The three ONNX files share the same weights and differ only in their static batch size. The TensorRT engine
requires a fixed batch size, so the node pads the ROI list with dummy entries up to the model's batch size
when fewer traffic lights are visible.
| Variant | Batch size | Default in Autoware |
|---|
tlr_car_ped_yolox_s_batch_1.onnx | 1 | |
tlr_car_ped_yolox_s_batch_4.onnx | 4 | |
tlr_car_ped_yolox_s_batch_6.onnx | 6 | yes (loaded by the package's launch file) |
Files
| File | Description |
|---|
tlr_car_ped_yolox_s_batch_1.onnx | YOLOX-s traffic light detector, static batch size 1 |
tlr_car_ped_yolox_s_batch_4.onnx | YOLOX-s traffic light detector, static batch size 4 |
tlr_car_ped_yolox_s_batch_6.onnx | YOLOX-s traffic light detector, static batch size 6 (default) |
tlr_labels.txt | Class labels: BACKGROUND, traffic_light, pedestrian_traffic_light |
deploy_metadata.yaml | Deployment metadata recording the artifact version of this repository |
TensorRT engines are not distributed here. TensorRT engines are specific to the GPU architecture and
TensorRT version they are built on and are not portable, so Autoware builds them locally from the ONNX files
on first launch (or via build_only:=true).
Inputs and outputs (as used by the node)
Inputs
| Topic | Type | Description |
|---|
~/input/image | sensor_msgs/msg/Image | The full size camera image |
~/input/rois | tier4_perception_msgs/msg/TrafficLightRoiArray | Coarse ROIs from the map-based detector |
~/expect/rois | tier4_perception_msgs/msg/TrafficLightRoiArray | Offset-free ROIs used to select the best detections |
Outputs
| Topic | Type | Description |
|---|
~/output/rois | tier4_perception_msgs/msg/TrafficLightRoiArray | The refined, accurate ROIs |
~/debug/exe_time_ms | autoware_internal_debug_msgs/msg/Float32Stamped | Inference time |
If no traffic light is detected inside a coarse ROI, the output ROI's x_offset, y_offset, height, and
width are set to 0. Detections from YOLOX are matched against the expect/rois array as a whole set, not
each ROI in isolation. Key node parameters (defaults): precision: fp16, score_thresh: 0.3,
nms_thresh: 0.65, gpu_id: 0.
Usage in Autoware
The node expects these artifacts under ~/autoware_data/ml_models/traffic_light_fine_detector/ and launches
with, e.g.:
1ros2 launch autoware_traffic_light_fine_detector traffic_light_fine_detector.launch.xml \
2 data_path:=$HOME/autoware_data/ml_models \
3 model_path:=$HOME/autoware_data/ml_models/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx \
4 label_path:=$HOME/autoware_data/ml_models/traffic_light_fine_detector/tlr_labels.txt
Add
build_only:=true to build the TensorRT engine from the ONNX as a one-off pre-task.
See the
package README
for the full parameter reference.
Training
- Base model: YOLOX-s from the official YOLOX repository,
starting from the published yolox_s.pth
pretrained weights.
- Fine-tuning: performed by TIER IV on around 17,000 internal images of Japanese traffic lights.
- Further training details (epochs, hyperparameters, evaluation metrics) are not publicly documented.
Limitations
- Fine-tuned on Japanese traffic lights; accuracy on traffic lights with different shapes, layouts, or
signaling conventions may drop without additional fine-tuning.
- The node is designed to run downstream of the map-based detector: it refines externally provided ROIs
rather than detecting traffic lights anywhere in the frame.
- The TensorRT engine has a static batch size; scenes with more simultaneous ROIs than the model's batch size
are processed in multiple inference passes.
Provenance
| |
|---|
| Original source | https://awf.ml.dev.web.auto/perception/models/tlr_yolox_s/v3/ |
| Version tag in this repository | v3.0 |
Citation
1@article{ge2021yolox,
2 title = {YOLOX: Exceeding YOLO Series in 2021},
3 author = {Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
4 journal = {arXiv preprint arXiv:2107.08430},
5 year = {2021}
6}
References
- [1] Ge et al., "YOLOX: Exceeding YOLO Series in 2021", arXiv:2107.08430, 2021.
- YOLOX repository: https://github.com/Megvii-BaseDetection/YOLOX
