Contrastive Learning for Lane Detection via Cross-Similarity

• URL: http://arxiv.org/abs/2308.08242v3
• Date: Fri, 1 Sep 2023 08:12:30 GMT
• Title: Contrastive Learning for Lane Detection via Cross-Similarity
• Authors: Ali Zoljodi, Sadegh Abadijou, Mina Alibeigi, Masoud Daneshtalab
• Abstract summary: Lane markings have strong shape priors, but their visibility is easily compromised. A large amount of data is required to train a lane detection approach that can withstand natural variations caused by low visibility. Our solution, Contrastive Learning for Lane Detection via cross-similarity (CLLD), is a self-supervised learning method.
• Score: 0.5735035463793009
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Detecting road lanes is challenging due to intricate markings vulnerable to unfavorable conditions. Lane markings have strong shape priors, but their visibility is easily compromised. Factors like lighting, weather, vehicles, pedestrians, and aging colors challenge the detection. A large amount of data is required to train a lane detection approach that can withstand natural variations caused by low visibility. This is because there are numerous lane shapes and natural variations that exist. Our solution, Contrastive Learning for Lane Detection via cross-similarity (CLLD), is a self-supervised learning method that tackles this challenge by enhancing lane detection models resilience to real-world conditions that cause lane low visibility. CLLD is a novel multitask contrastive learning that trains lane detection approaches to detect lane markings even in low visible situations by integrating local feature contrastive learning (CL) with our new proposed operation cross-similarity. Local feature CL focuses on extracting features for small image parts, which is necessary to localize lane segments, while cross-similarity captures global features to detect obscured lane segments using their surrounding. We enhance cross-similarity by randomly masking parts of input images for augmentation. Evaluated on benchmark datasets, CLLD outperforms state-of-the-art contrastive learning, especially in visibility-impairing conditions like shadows. Compared to supervised learning, CLLD excels in scenarios like shadows and crowded scenes.

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