Semi-Supervised Image-to-Image Translation for Lane Detection in Rain

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Semi-Supervised Image-to-Image Translation for Lane Detection in Rain
Title:
Semi-Supervised Image-to-Image Translation for Lane Detection in Rain
Journal Title:
2022 IEEE Conference on Intelligent Transportation Systems (ITSC 2022)
DOI:
Publication Date:
26 September 2022
Citation:
Proceedings of the 2022 IEEE Conference on Intelligent Transportation Systems (ITSC 2022)
Abstract:
Impressive progress has recently been made in deep learning based lane detection for the autonomous vehicle domain using an in-car camera. However, relatively little attention was paid to lane detection under bad weather conditions. The general difficulty stems from the water on the road or raindrops remaining on the windscreen and hampering lane detectability. In this paper, we propose a lane enhancement approach to improve lane detection accuracy under rain. We formulate image enhancement as an image-to-image translation problem, and devise semi-supervised techniques to efficiently learn from an image set containing images from source domain (rain images) and target domain (clear images). Our semisupervision approach differs from the conventional unsupervised image-to-image translation, in that a small amount of labelled rain images are added to the target domain in order to guide the translation to focus on enhancing the lanes while preserving the background. Specifically, we first compute the road regions in an image using vanishing points from camera intrinsic matrix. We then define a loss function using the road regions as constrains, in order to enforce lane-aware image generation. As a result, new rain images are generated by highlighting the lanes explicitly in thick bright lines. Our empirical results show that using only a few labelled images, our proposed semi-supervised learning is able to enhance lanes efficiently and improving lane detection significantly.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Singapore government’s Research - Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain) and administered by the Agency for Science, Technology and Research
Grant Reference no. : I2001E0063
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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