ultra-fast-lane-detection

Ultra-Fast-Lane-Detection

Input

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

Input shape: (1, 288, 800, 3)

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 ultra-fast-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 ultra-fast-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 ultra-fast-lane-detection.py --video VIDEO_PATH

By adding the --arch option, you can select the model architecture from culane and tusimple.

Reference

Ultra-Fast-Lane-Detection

Framework

Pytorch

Model Format

ONNX opset = 11

Netron

tusimple_18.onnx.prototxt

culane_18.onnx.prototxt