Vehicle-CV-ADAS

Example scripts for the detection of lanes using the ultra fast lane detection v2 model in ONNX/TensorRT.

Example scripts for the detection of objects using the YOLOv5/YOLOv5-lite/YOLOv6/YOLOv7/YOLOv8/YOLOv9/YOLOv10/EfficientDet model in ONNX/TensorRT.

Add ByteTrack to determine the driving direction of ID vehicles and perform trajectory tracking.

➤ Contents

1) Requirements

2) Examples

3) Demo

4) License

!ADAS on video

➤ Requirements

Python 3.7+
OpenCV, Scikit-learn, onnxruntime, pycuda and pytorch.
Install :

The requirements.txt file should list all Python libraries that your notebooks depend on, and they will be installed using:
```
pip install -r requirements.txt
```

➤ Examples

Download YOLO Series Onnx model :

Use the Google Colab notebook to convert

Model release version Link

YOLOv5 v6.2

YOLOv6/Lite 0.4.0

YOLOv7 v0.1

YOLOv8 8.1.27

YOLOv9 v0.1

YOLOv10 8.2.41
Convert Onnx to TenserRT model :

Need to modify onnx_model_path and trt_model_path before converting.
```
python convertOnnxToTensorRT.py -i <path-of-your-onnx-model>  -o <path-of-your-trt-model>
```
Quantize ONNX models :

Converting a model to use float16 instead of float32 can decrease the model size.
```
python onnxQuantization.py -i <path-of-your-onnx-model>
```

Model	release version	Link
YOLOv5	`v6.2`
YOLOv6/Lite	`0.4.0`
YOLOv7	`v0.1`
YOLOv8	`8.1.27`
YOLOv9	`v0.1`
YOLOv10	`8.2.41`

Video Inference :

Setting Config :

Note : can support onnx/tensorRT format model. But it needs to match the same model type.

lane_config = {
 "model_path": "./TrafficLaneDetector/models/culane_res18.trt",
 "model_type" : LaneModelType.UFLDV2_CULANE
}

object_config = {
 "model_path": './ObjectDetector/models/yolov8l-coco.trt',
 "model_type" : ObjectModelType.YOLOV8,
 "classes_path" : './ObjectDetector/models/coco_label.txt',
 "box_score" : 0.4,
 "box_nms_iou" : 0.45
}

Target	Model Type	Describe
Lanes	`LaneModelType.UFLD_TUSIMPLE`	Support Tusimple data with ResNet18 backbone.
Lanes	`LaneModelType.UFLD_CULANE`	Support CULane data with ResNet18 backbone.
Lanes	`LaneModelType.UFLDV2_TUSIMPLE`	Support Tusimple data with ResNet18/34 backbone.
Lanes	`LaneModelType.UFLDV2_CULANE`	Support CULane data with ResNet18/34 backbone.
Object	`ObjectModelType.YOLOV5`	Support yolov5n/s/m/l/x model.
Object	`ObjectModelType.YOLOV5_LITE`	Support yolov5lite-e/s/c/g model.
Object	`ObjectModelType.YOLOV6`	Support yolov6n/s/m/l, yolov6lite-s/m/l model.
Object	`ObjectModelType.YOLOV7`	Support yolov7 tiny/x/w/e/d model.
Object	`ObjectModelType.YOLOV8`	Support yolov8n/s/m/l/x model.
Object	`ObjectModelType.YOLOV9`	Support yolov9t/s/m/c/e model.
Object	`ObjectModelType.YOLOV10`	Support yolov10n/s/m/b/l/x model.
Object	`ObjectModelType.EfficientDet`	Support efficientDet b0/b1/b2/b3 model.