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PolyLaneNet: Lane Estimation via Deep Polynomial Regression

Input

Images with the same aspect ratio as 360×640. This model detect lane from inputs.

  • input image
    入力画像

Output

Images which the detected lane in input images is colored in green.

  • output image
    Four green lines mean each detected lane which model predicted and numbers attached to each line mean line number. 出力画像

Usage

  • Image mode (image to image)
    You run sample script as below if your desired file is in {Path to polylanenet}/input/image/8.jpg.
$python3 polylanenet.py --input input.jpg
  • Video mode (video to video)
    You run sample script as below if your desired file is in {Path to polylanenet}/input/video/video1.mp4
$python3 polylanenet.py --video input.mp4

Reference

  • Repository
    PolyLaneNet

  • Input images and videos
    Input images are part of TuSimple dataset and input videos are created by using TuSimple images.

Framework

PyTorch 1.9.0

Model Format

ONNX opset = 11

Netron

polylanenet.onnx.prototxt