A fuzzy feature integration-enhanced network for surface defect detection of no-service rails

Abstract

Surface defect detection on no-service rails is crucial for ensuring the safety and reliability of industrial manufacturing processes. Vision-based detection methods have seen significant progress in this domain. However, this task still faces significant challenges from the following aspects: (1) The great amounts of noise in rail surface defect images; (2) Great similarity between the foreground and background of defect images. Fuzzy logic, a significant technique in the automatic control, is effective for handling uncertain and imprecise features to improve the stability of detection systems. In this paper, we propose a novel approach by integrating fuzzy logic into deep neural networks for rail surface defect detection. First, we introduce a fuzzy logic-based feature enhancement module, where a Gaussian-based fuzzy strategy is utilized to improve feature representation. Next, we devise a fuzzy logic-based loss function tailored for fuzzy features which ensures that the fuzzy representation of features is beneficial for defect segmentation. Experimental validation using both RGB and RGB-depth images demonstrates the competitive and promising performance of our proposed approach compared to state-of-the-art models. Extensive validation on strip steel surface defect detection and salient object detection in natural images further confirms the effectiveness of our model and the application of fuzzy logic. Furthermore, this paper discusses the research significance and potential applications of the proposed methodology across various domains.