codes-for-lane-detection

Codes-for-Lane-Detection

Input

(Image from https://github.com/czming/RONELD-Lane-Detection/tree/main/example/00000.jpg)

Input shape: (1, 3, 208, 976) for erfnet, (1, 288, 800, 3) for scnn

Output

Usage

Automatically downloads the onnx and prototxt files on the first run. It is necessary to be connected to the Internet while downloading.

For the sample image,

$ python3 codes-for-lane-detection.py

If you want to specify the input image, put the image path after the --input option.
You can use --savepath option to change the name of the output file to save.

$ python3 codes-for-lane-detection.py --input IMAGE_PATH --savepath SAVE_IMAGE_PATH

By adding the --video option, you can input the video.
If you pass 0 as an argument to VIDEO_PATH, you can use the webcam input instead of the video file.

$ python3 codes-for-lane-detection.py --video VIDEO_PATH

By adding the --arch option, you can select the model architecture from erfnet and scnn.

Reference

Codes-for-Lane-Detection

ERFNet-CULane-PyTorch

Spatial As Deep: Spatial CNN for Traffic Scene Understanding

Framework

ERFNet : Pytorch

SCNN : Tensorflow 1.13.2

Model Format

ONNX opset = 11

Netron

erfnet.opt.onnx.prototxt

SCNN_tensorflow.opt.onnx.prototxt