{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:07:05Z","timestamp":1790075225540,"version":"4.0.1"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Automotive Chip Testing and Evaluation, State Administration for Market Regulation","award":["QCXP202504"],"award-info":[{"award-number":["QCXP202504"]}],"id":[{"id":"https:\/\/ror.org\/00aaqtw69","id-type":"ROR","asserted-by":"crossref"}]},{"award":["QCXP202504"],"award-info":[{"award-number":["QCXP202504"]}],"id":[{"id":"https:\/\/ror.org\/00aaqtw69","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate detection of small and weak defects is essential for ensuring the manufacturing quality and operational reliability of printed circuit boards (PCBs). Existing detectors, however, remain constrained by insufficient fine-grained feature representation, interference from repetitive conductive backgrounds, localization instability, and excessive computational complexity. To address these limitations, a lightweight defect detection model, termed Compact Recalibration and Fusion YOLO (CRF-YOLO), is developed based on YOLO11n. C3k2-Lite integrates partial-channel spatial modeling with cross-stage feature aggregation, reducing redundant computation while retaining essential defect information. The Residual Feature Fusion Attention module (RFFA) performs heterogeneous defect-evidence decomposition by jointly encoding positional, boundary, connectivity, and texture cues. Independently gated evidence aggregation and dual-dimensional feature recalibration strengthen weak contour interruptions, abnormal conductive connections, and subtle texture disturbances embedded in complex circuit backgrounds. The Residual Cross-Fusion module (RCF) establishes discrepancy-guided dual-stream feature reconciliation between the original and attention-enhanced representations. Location-adaptive feature selection and structure-aware detail reconstruction preserve low-amplitude defect cues while selectively incorporating discriminative information. Shape-NWD is adopted to improve the localization stability of small, elongated, and geometrically irregular defects. Experimental results show that CRF-YOLO achieves a Precision of 95.53%, a Recall of 91.26%, an mAP@0.5 of 94.46%, and an mAP@0.5:0.95 of 51.74%, with 2.175 M parameters and 6.0 GFLOPs. Compared with representative mainstream object detectors, the proposed model delivers superior overall detection performance while maintaining more favorable lightweight characteristics. A browser-based inspection interface is also implemented to support image uploading, automated defect detection, and result visualization, demonstrating the practical deployment potential of the proposed approach.<\/jats:p>","DOI":"10.3390\/s26185984","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:21:48Z","timestamp":1790072508000},"page":"5984","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight PCB Defect Detection Method Based on Heterogeneous Feature Enhancement and Discrepancy-Guided Fusion"],"prefix":"10.3390","volume":"26","author":[{"given":"Yujie","family":"Pei","sequence":"first","affiliation":[{"name":"Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3840-0275","authenticated-orcid":false,"given":"Xuehong","family":"Gao","sequence":"additional","affiliation":[{"name":"Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"given":"Shenyuan","family":"Gao","sequence":"additional","affiliation":[{"name":"Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Yongchang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Automotive Chip Testing and Evaluation, State Administration for Market Regulation, Beijing National New Energy Vehicle Technology Innovation Center Co., Ltd., Beijing 100176, China"},{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Automotive Chip Testing and Evaluation, State Administration for Market Regulation, Beijing National New Energy Vehicle Technology Innovation Center Co., Ltd., Beijing 100176, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Guozhong","family":"Huang","sequence":"additional","affiliation":[{"name":"Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"given":"Lili","family":"Lei","sequence":"additional","affiliation":[{"name":"Key Laboratory of Automotive Chip Testing and Evaluation, State Administration for Market Regulation, Beijing National New Energy Vehicle Technology Innovation Center Co., Ltd., Beijing 100176, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.jmsy.2023.08.019","article-title":"Review of vision-based defect detection research and its perspectives for printed circuit board","volume":"70","author":"Zhou","year":"2023","journal-title":"J. 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