{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:58:57Z","timestamp":1777705137869,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,1,10]]},"abstract":"<jats:p>Defect detection in mobile phone cameras constitutes a critical aspect of the manufacturing process. Nonetheless, this task remains challenging due to the complexities introduced by intricate backgrounds and low-contrast defects, such as minor scratches and subtle dust particles. To address these issues, a Bilateral Feature Fusion Network (BFFN) has been proposed. This network incorporates a bilateral feature fusion module, engineered to enrich feature representation by fusing feature maps from multiple scales. Such fusion allows the capture of both fine and coarse-grained details inherent in the images. Additionally, a Self-Attention Mechanism is deployed to garner more comprehensive contextual information, thereby enhancing feature discriminability. The proposed Bilateral Feature Fusion Network has been rigorously evaluated on a dataset of 12,018 mobile camera images. Our network surpasses existing state-of-the-art methods, such as U-Net and Deeplab V3+, particularly in mitigating false positive detection caused by complex backgrounds and false negative detection caused by slight defects. It achieves an F1-score of 97.59%, which is 1.16% better than Deeplab V3+ and 0.99% better than U-Net. This high level of accuracy is evidenced by an outstanding precision of 96.93% and recall of 98.26%. Furthermore, our approach realizes a detection speed of 63.8 frames per second (FPS), notably faster than Deeplab V3+ at 57.1 FPS and U-Net at 50.3 FPS. This enhanced computational efficiency makes our network particularly well-suited for real-time defect detection applications within the realm of mobile camera manufacturing.<\/jats:p>","DOI":"10.3233\/jifs-232664","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T11:34:15Z","timestamp":1702380855000},"page":"2585-2594","source":"Crossref","is-referenced-by-count":0,"title":["A bilateral feature fusion network for defect detection on mobile cameras"],"prefix":"10.1177","volume":"46","author":[{"given":"Cong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering, Yancheng Institute of Technology, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"She","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Yancheng Institute of Technology, Jiangsu, 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