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RGB-D Rail Surface Defect Inspection Driven by Conditional Diffusion Architecture and Frequency Knowledge

IEEE Sensors Journal 2025年第10期25卷 18334-18343页

作者： He, Zhihao Li, Gongyang Liu, Zhi Shanghai University School of Communication and Information Engineering Shanghai200444 China Anhui Province Key Laboratory of Machine Vision Detection and Perception Wuhu241000 China Wenzhou Institute of Shanghai University Wenzhou325000 China Shanghai University Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai200444 China

RGB-D Rail Surface Defect Inspection (RSDI) is a critical measure for ensuring transportation safety. It improves inspection accuracy by using depth maps, but the issue of poor-quality depth maps in rail defect datasets is often overlooked. Additionally, the similarity between the foreground and background has always been a persistent challenge in RGB-D RSDI. In this article, we propose a novel network, termed DiffRSDI to overcome these challenges in RGB-D RSDI, which treats RGB-D RSDI as a mask generation task using a conditional diffusion architecture. Specifically, DiffRSDI consists of two key components: Cross-modal Cyclic Enhancement (CCE) module and Frequency-aware Alternate Fusion (FAF) module. CCE module is based on the cyclic enhancement approach, and aims to eliminate interference from poor-quality depth maps and aggregate cross-modal features. FAF module overcomes the similarity between the foreground and background in the frequency-domain, and achieves cross-scale fusion in the fused features. Comprehensive experiments conducted on the NEU RSDDS-AUG dataset confirm that our method achieves superior performance in comparison to 14 related methods. The DiffRSDI code and results are available at https://***/zeroyi37/DiffRSDI. © 2001-2012 IEEE.

关键词： Railroad accidents

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