{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:33:50Z","timestamp":1780623230086,"version":"3.54.1"},"reference-count":24,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T00:00:00Z","timestamp":1770249600000},"content-version":"unspecified","delay-in-days":35,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["AIEDAM"],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>To address the challenges of low detection accuracy, missed detections, and high false detection rates for small targets in PCB defect detection tasks, this study proposes an enhanced YOLOv8 methodology incorporating feature focusing and multi-scale fusion techniques. Initially, a lightweight GTADH module is integrated into the detection head of YOLOv8, employing a shared convolution and task alignment mechanism to minimize model parameters while enhancing classification and localization accuracy. Subsequently, an adaptive feature-focusing module is introduced into the feature fusion network to bolster the detection capabilities for small targets via multi-scale feature fusion. Finally, the reverse residual moving block (iRMB) and attention mechanisms are combined within the backbone network to facilitate efficient extraction and fusion of feature information, preserving finer details of small targets. Experimental results demonstrate that the Improved YOLO algorithm achieves a 1.3% increase in detection accuracy and a 7.3% enhancement in mAP50:90 evaluation standards compared to the original YOLOv8s algorithm on the PCB defect dataset, while also reducing model size by 60%, thus showcasing its effectiveness in small target detection tasks.<\/jats:p>","DOI":"10.1017\/s0890060426100213","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T05:45:38Z","timestamp":1770270338000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":1,"title":["Small target detection of surface defects on PCB boards: an improved YOLO method integrating attention mechanism and multi-scale feature focusing"],"prefix":"10.1017","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7789-3369","authenticated-orcid":false,"given":"Wenxue","family":"Zhang","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01285e189","id-type":"ROR","asserted-by":"publisher"}],"name":"Xiamen University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingjing","family":"Lin","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01285e189","id-type":"ROR","asserted-by":"publisher"}],"name":"Xiamen University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saiqiang","family":"Wei","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01285e189","id-type":"ROR","asserted-by":"publisher"}],"name":"Xiamen University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junxi","family":"Wu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01285e189","id-type":"ROR","asserted-by":"publisher"}],"name":"Xiamen University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"S0890060426100213_r9","first-page":"156","article-title":"Research on some key technologies of intelligent manufacturing","volume":"28","author":"Min","year":"2018","journal-title":"Science and Technology Innovation and Application"},{"key":"S0890060426100213_r8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"S0890060426100213_r16","first-page":"7464","volume-title":"Proceedings of the IEEE\/CVFConference on Computer Vision and Pattern Recognition","author":"Wang","year":"2023"},{"key":"S0890060426100213_r17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3198994"},{"key":"S0890060426100213_r19","first-page":"2791","article-title":"Defect detection of bare PCB based on improved YOLOv7 algorithm","volume":"53","author":"Xianyong","year":"2023","journal-title":"Radio Engineering"},{"key":"S0890060426100213_r20","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3208580"},{"key":"S0890060426100213_r4","unstructured":"Zheng, Ge , Songtao, Liu , Feng, Wang , Zeming, Li , Jian, Sun (2021) Yolox: Exceeding yolo series in 2021 [EB\/OL]. [2023-05-17]. https:\/\/arxiv.org\/pdf\/2107.08430.pdf"},{"key":"S0890060426100213_r10","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27\u201330 June 2016","author":"Redmon","year":"2016"},{"key":"S0890060426100213_r23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115673"},{"key":"S0890060426100213_r1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2004.10934"},{"key":"S0890060426100213_r18","first-page":"1","article-title":"Research status and Prospect of PCB defect detection algorithm based on machine vision","volume":"43","author":"Wu","year":"2022","journal-title":"Chinese Journal of Scientific Instrument"},{"key":"S0890060426100213_r21","first-page":"3505008","article-title":"Collaborative learning classification model for PCBs defect detection against image and label uncertainty","volume":"72","author":"Yu","year":"2023","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"S0890060426100213_r6","first-page":"580","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 24\u201328 June 2014","author":"Girshick","year":"2014"},{"key":"S0890060426100213_r14","doi-asserted-by":"publisher","DOI":"10.3390\/s22207971"},{"key":"S0890060426100213_r24","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3214306"},{"key":"S0890060426100213_r13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"S0890060426100213_r22","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03633-x"},{"key":"S0890060426100213_r3","first-page":"3490","volume-title":"2021 IEEE\/CVF International Conference on Computer Vision","author":"Chengjian","year":"2021"},{"key":"S0890060426100213_r15","first-page":"13029","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Wang","year":"2021"},{"key":"S0890060426100213_r5","first-page":"1440","volume-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7\u201313December 2015","author":"Girshick","year":"2015"},{"key":"S0890060426100213_r2","doi-asserted-by":"publisher","DOI":"10.1049\/trit.2019.0019"},{"key":"S0890060426100213_r7","volume-title":"Research on Surface Defect Detection of Bare PCB Board Based on Deep Learning","author":"Hongyan","year":"2022"},{"key":"S0890060426100213_r12","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1804.02767"},{"key":"S0890060426100213_r11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"}],"container-title":["Artificial Intelligence for Engineering Design, Analysis and Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0890060426100213","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T05:45:41Z","timestamp":1770270341000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0890060426100213\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":24,"alternative-id":["S0890060426100213"],"URL":"https:\/\/doi.org\/10.1017\/s0890060426100213","relation":{},"ISSN":["0890-0604","1469-1760"],"issn-type":[{"value":"0890-0604","type":"print"},{"value":"1469-1760","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"\u00a9 The Author(s), 2026. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:\/\/creativecommons.org\/licenses\/by\/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}],"article-number":"e2"}}