CSANet: Contour and Semantic Feature Alignment Fusion Network for Rail Surface Defect Detection

Rail surface defect detection for traffic safety has received considerable attention. With the development of deep learning, numerous methods for combining RGB and depth information have been proposed. However, these methods directly fuse raw features extracted from the backbone, which can lead to ineffective use of the complementary information of the two modalities. In this study, we developed a contour and semantic feature alignment fusion network (CSANet) with bidirectional feature alignment to explore the internal consistency of cross-modal features from both contour and semantic perspectives. First, an adjacency contour feature extraction module was designed to capture high-quality contour information from adjacent low-level features. Second, an attention-aware graph convolution embedded semantic feature extraction module was designed to explore long-range dependencies and extract semantic information. Third, a bidirectional alignment mechanism was designed to explore the internal consistency of contours and semantics between bimodal features. Experimental results on the industrial RGB-D dataset (NEU RSDDS-AUG) revealed that the proposed CSANet outperformed 12 state-of-the-art algorithms in four evaluation metrics.