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.

翻译：检测道路车道因脆弱易受不利条件影响的复杂标记而具有挑战性。车道标记具有强形状先验，但其可见性容易受损。光照、天气、车辆、行人及老化色彩等因素会干扰检测。需要大量数据来训练能够承受低可见度引起的自然变化的车道检测方法。这是因为存在多种车道形状和自然变化。我们的解决方案——基于交叉相似度的车道检测对比学习（CLLD）——是一种自监督学习方法，通过增强车道检测模型对导致车道低可见度的真实世界条件的鲁棒性来应对这一挑战。CLLD是一种新颖的多任务对比学习，通过将局部特征对比学习与本文新提出的交叉相似度操作相结合，训练车道检测方法即使在低可见度情况下也能检测车道标记。局部特征对比学习专注于提取小图像部分的特征，这对于定位车道段是必要的，而交叉相似度则通过捕捉全局特征来利用周围环境检测被遮挡的车道段。我们通过随机遮蔽输入图像的部分区域进行数据增强来提升交叉相似度。在基准数据集上的评估表明，CLLD优于最先进的对比学习方法，特别是在阴影等影响可见度的条件下。与监督学习相比，CLLD在阴影和拥挤场景等情况下表现卓越。