Specificity autocorrelation integration network for surface defect detection of no-service rail

Rails are critical to the safe transportation of railway system, and their surface quality is a vital aspect to consider. Existing defect detection methods struggle to identify irregular defect boundaries and distinguish the similarity of foreground and background. To address these issues, depth images are introduced to detect rail defects. However, the existing RGB-D SOD methods usually fuse two modalities without considering modality-specific characteristics. In this paper, we propose a specificity autocorrelation integration network (SAINet) for surface defect detection of rails. SAINet enhances defect detection performance by exploring autocorrelation features of a single modality and the specificity of each modality. Two decoders are carefully designed to capture the specific characteristics of each modality. Moreover, we propose a cross-modal autocorrelation attention fusion (CAAF) to effectively utilize the two modalities of information. It obtains autocorrelation features of RGB images through dilated convolution and attention modules, introducing depth features to locate defects more accurately. We design a multi-modal feature integration block (MFIB) to supplement the cross-modal features with modality-specific features output by each individual decoder, in order to boost SOD performance. SAINet's performance is verified on the NEU RSDDS-AUG dataset. Our network achieves the best results compared to the 25 state-of-the-art methods. We also validate SAINet's generalization performance on six other benchmark datasets, where the experiments show it competes well on these datasets. The code and results are available at https://github.com/VDT-2048/SAINet.