School of Science, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
Sensors (Basel). 2023 May 19;23(10):4917. doi: 10.3390/s23104917.
Lane detection is a crucial task in the field of autonomous driving, as it enables vehicles to safely navigate on the road by interpreting the high-level semantics of traffic signs. Unfortunately, lane detection is a challenging problem due to factors such as low-light conditions, occlusions, and lane line blurring. These factors increase the perplexity and indeterminacy of the lane features, making them hard to distinguish and segment. To tackle these challenges, we propose a method called low-light enhancement fast lane detection (LLFLD) that integrates the automatic low-light scene enhancement network (ALLE) with the lane detection network to improve lane detection performance under low-light conditions. Specifically, we first utilize the ALLE network to enhance the input image's brightness and contrast while reducing excessive noise and color distortion. Then, we introduce symmetric feature flipping module (SFFM) and channel fusion self-attention mechanism (CFSAT) to the model, which refine the low-level features and utilize more abundant global contextual information, respectively. Moreover, we devise a novel structural loss function that leverages the inherent prior geometric constraints of lanes to optimize the detection results. We evaluate our method on the CULane dataset, a public benchmark for lane detection in various lighting conditions. Our experiments show that our approach surpasses other state of the arts in both daytime and nighttime settings, especially in low-light scenarios.
车道检测是自动驾驶领域的一项关键任务，因为它能够使车辆通过解释交通标志的高级语义安全地在道路上行驶。不幸的是，车道检测是一个具有挑战性的问题，因为低光照条件、遮挡和车道线模糊等因素。这些因素增加了车道特征的复杂性和不确定性，使得它们难以区分和分割。为了解决这些挑战，我们提出了一种名为低光增强快速车道检测（LLFLD）的方法，该方法将自动低光场景增强网络（ALLE）与车道检测网络集成在一起，以提高低光条件下的车道检测性能。具体来说，我们首先利用 ALLE 网络来增强输入图像的亮度和对比度，同时减少过度的噪声和颜色失真。然后，我们引入了对称特征翻转模块（SFFM）和通道融合自注意力机制（CFSAT）到模型中，分别对低层次特征进行细化，并利用更丰富的全局上下文信息。此外，我们设计了一种新的结构损失函数，利用车道的固有几何约束来优化检测结果。我们在 CULane 数据集上评估了我们的方法，该数据集是用于各种光照条件下车道检测的公共基准。我们的实验表明，我们的方法在白天和夜间环境下都优于其他最先进的方法，特别是在低光场景下。
