xmba15 / rail_marking

proof-of-concept program that detects rail-track with semantic segmentation for autonomous train system
MIT License
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autonomous-driving bisenetv2 railsem19 railtrack-detection railway-detection semantic-segmentation

DOI

📝 proof-of-concept rail marking detections for autonomous train system


This project implements rail-track detection using fast semantic segmentation for high-resolution images from bisenetv2 algorithm.

The author of bisenetv2 has not made the official implementation public so the implementation in this project might yeild different performance with the network introduced in the original paper.

This project trains bisenetv2 on a modified version of RailSem19 dataset with only three labels ("rail-raised", "rail-track", "background"). Please follow here if you want to download the original dataset.

sample video result

:tada: TODO


🎛 Dependencies


    conda env create --file environment.yml

:running: How to Run


Download trained weights from HERE.

    python ./scripts/segmentation/test_one_image.py -snapshot [path/to/trained/weight] -image_path [path/to/image/path]

Sample segmentation result:

sample rail result

Download sample video from HERE.

The video was originally downloaded from this youtube channel.

    python ./scripts/segmentation/test_video.py -snapshot [path/to/trained/weight] -video_path [path/to/video/path]

The test result can be seen as the gif image above. The frame rate could reach 40fps, faster than needed for an autonomous system.

:gem: References