traffic_light_ssd_fine_detector#
Purpose#
It is a package for traffic light detection using MobileNetV2 and SSDLite.
The trained model is based on pytorch-ssd.
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#
Onnx model#
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.