Gradient-based edge effects on lane marking detection using a deep learning-based approach

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