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

➤ 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>
  

• 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.

  • Run :

  python demo.py
  

➤ Demo

• Demo Youtube Video

• Display

  

• Front Collision Warning System (FCWS)

  

• Lane Departure Warning System (LDWS)

  

• Lane Keeping Assist System (LKAS)

  

➤ License

WiFi Analyzer is licensed under the GNU General Public License v3.0 (GPLv3).

GPLv3 License key requirements :

• Disclose Source
• License and Copyright Notice
• Same License
• State Changes