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Next, a conditional GAN (pix2pix) was employed as the backbone architecture for the detection model. This generated repaired images of the defective panels, which were subtracted from the original images to produce difference maps. Sobel edge features were subsequently extracted from both the input image and its corresponding repaired image, and the edge features from both sources were differentially processed. By weighting and fusing the edge feature differences with the difference map, the detection and localization of panel defects were achieved. The experimental results validate that the proposed algorithm can efficiently detect and locate defects precisely on socket panels even in the presence of reflective interference while maintaining high accuracy and reliability.<\/jats:p>","DOI":"10.1142\/s0219691326500062","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T08:25:33Z","timestamp":1769761533000},"source":"Crossref","is-referenced-by-count":0,"title":["Conditional GAN-based approach for socket panel surface defect inspection"],"prefix":"10.1142","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5283-7126","authenticated-orcid":false,"given":"Gang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer and Data Engineering, NingboTech University, Ningbo, P. R. 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