Lane detection is part of the advanced driver assistance system (ADAS) equipped in intelligent vehicles. The system provides the driver with significant geometric information of the road ahead. Numerous deep learning techniques have been employed in lane detection because of the simplicity, ease, and efficiency of these techniques in learning discriminative features from RGB (red, green, and blue) images. However, existing works have rarely considered detecting lane markings during bad weather conditions, which could reduce lane detection performance. Hence, this paper proposed a Fully Convolutional Network (FCN) model with RGB and Canny edge detection used as the model’s spatial input. The proposed platform was developed using two scenarios: FCN-RGB-edge and FCN-edge. The model development was divided into three stages, namely data acquisition, platform development, and benchmarking against existing methods and data. Both scenarios using the proposed method yielded a 4% improvement compared to the original FCN-RGB images (i.e., the previous method). The Canny edge detection method successfully extracted necessary information from the images and neglected the water drops in rainy conditions by treating them as noise. In summary, the proposed method has the potential to boost the performance of the ADAS system in detecting lane markings in rainy conditions.

车道检测是智能车辆配备的高级驾驶员辅助系统（ADAS）的一部分。该系统为驾驶员提供了前方道路的重要几何信息。由于这些技术在从RGB（红色，绿色和蓝色）图像中学习区分特征的简单性，便捷性和高效性，已在车道检测中采用了许多深度学习技术。但是，现有工作很少考虑在恶劣天气条件下检测车道标记，这可能会降低车道检测性能。因此，本文提出了使用RGB和Canny边缘检测作为模型的空间输入的完全卷积网络（FCN）模型。所提出的平台是使用两种方案开发的：FCN-RGB-edge和FCN-edge。模型开发分为三个阶段，即数据获取，平台开发，并根据现有方法和数据进行基准测试。与原始的FCN-RGB图像（即先前的方法）相比，使用提议的方法的两种情况均产生了4％的改善。Canny边缘检测方法成功地从图像中提取了必要的信息，并在雨天将水滴视为噪声而忽略了水滴。总之，所提出的方法有可能提高ADAS系统在雨天条件下检测车道标记的性能。Canny边缘检测方法成功地从图像中提取了必要的信息，并在雨天将水滴视为噪声而忽略了水滴。总之，所提出的方法有可能提高ADAS系统在雨天条件下检测车道标记的性能。Canny边缘检测方法成功地从图像中提取了必要的信息，并在雨天将水滴视为噪声而忽略了水滴。总之，所提出的方法有可能提高ADAS系统在雨天条件下检测车道标记的性能。