SALMNet: A Structure-Aware Lane Marking Detection Network
Xuemiao Xu, Tianfei Yu, Xiaowei Hu, Wing W. Y. Ng, Pheng‐Ann Heng
Abstract
Lane marking detection is a fundamental task, which serves as an important prerequisite for automatic driving or driver-assistance systems. However, the complex and uncontrollable driving road environment as well as the discontinuous lane marking appearance make this task challenging. In this work, a novel deep neural network architecture is presented to detect lane markings in a complex environment by analyzing their structure information. There are two contributions to the network design. Firstly, a semantic-guided channel attention (SGCA) module is developed to select the low-level features of a deep convolutional neural network by taking the high-level features as the guidance. Secondly, a pyramid deformable convolution (PDC) module is formulated to enlarge the receptive fields and to capture the complex structures of lane markings by applying deformable convolutions on multiple feature maps with different scales. Hence, our network can better reduce false detection and enhance lane marking structures simultaneously. The experimental results on three benchmark datasets for lane marking detection show that our method outperforms other methods on all the benchmark datasets.