{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T07:18:28Z","timestamp":1783408708264,"version":"3.54.6"},"reference-count":27,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"vor","delay-in-days":186,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302231"],"award-info":[{"award-number":["62302231"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>With the rapid development of the electronic manufacturing industry, printed circuit boards (PCBs) are critical components in electronic systems, and the accuracy of their defect detection directly determines product quality and production efficiency. However, existing mainstream methods still exhibit several key limitations in PCB defect detection, including poor adaptability to multiscale defects, high computational complexity of attention mechanisms, and high missed detection rates for small targets. These limitations lead to insufficient detection accuracy in complex industrial scenarios, failing to meet the requirements of modern production. To address the above problems, this paper proposes a PCB defect detection algorithm based on YOLOv11\u2010DDP. The algorithm introduces a dynamic feature fusion module to improve model generalization, embeds the DAT attention mechanism to effectively balance accuracy and efficiency, and adopts a P2 detection head to enhance the perception ability of tiny defects. Experimental results show that the proposed algorithm achieves an mAP@0.5 of 94.1% on the PCB defect dataset, representing a 3.7% improvement over the original YOLOv11 model. Meanwhile, mAP@0.5:0.95 is increased by 1.8%, precision by 5.3%, and recall by 2.6%. The proposed method effectively mitigates the problem of insufficient detection accuracy in PCB defect detection and can meet the high\u2010precision detection requirements in complex industrial scenarios.<\/jats:p>","DOI":"10.1155\/jece\/2638110","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T06:50:29Z","timestamp":1783407029000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLOv11\u2010DDP: An Accuracy\u2010Enhanced PCB Defect Detection Algorithm Featuring Dynamic Feature Fusion and DAT Attention Mechanism"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9184-5198","authenticated-orcid":false,"given":"Lianlian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9405-0792","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6576-1838","authenticated-orcid":false,"given":"Xiaorui","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5079-5266","authenticated-orcid":false,"given":"Yujin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1631-7841","authenticated-orcid":false,"given":"Jie","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9046-2886","authenticated-orcid":false,"given":"Mengmeng","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8941-2041","authenticated-orcid":false,"given":"Xiaoru","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0523-8665","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-010-2567-9"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2014.08.218"},{"key":"e_1_2_11_3_2","doi-asserted-by":"crossref","unstructured":"LochG. N. SzymanskiC. andStemmerM. R. Evaluation of SIFT in Machine Vision Applied to Industrial Automation Proceedings of the 2013 IEEE International Conference on Industrial Informatics (INDIN) 2013 Bochum IEEE 414\u2013419.","DOI":"10.1109\/INDIN.2013.6622920"},{"key":"e_1_2_11_4_2","doi-asserted-by":"crossref","unstructured":"SzymanskiC.andStemmerM. R. Automated PCB Inspection in Small Series Production Based on SIFT Algorithm Proceedings of the 2015 IEEE International Symposium on Industrial Electronics (ISIE) 2015 Buzios IEEE 594\u2013599.","DOI":"10.1109\/ISIE.2015.7281535"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1049\/joe.2018.8274"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsm.2013.2261566"},{"key":"e_1_2_11_7_2","first-page":"217","article-title":"Lightweight Multi-CR YOLO Network for Printed Circuit Board Defect Detection","volume":"37","author":"Jiang Y. Y.","year":"2024","journal-title":"Journal of Electronic Measurement and Instrumentation"},{"key":"e_1_2_11_8_2","article-title":"Printed Circuit Board Defect Recognition Based on Dense YOLOv3","volume":"45","author":"Yang J.","year":"2022","journal-title":"Journal of Beijing University of Posts and Telecommunications"},{"key":"e_1_2_11_9_2","article-title":"Lightweight PCB Surface Defect Detection Algorithm","volume":"47","author":"Zhang G.","year":"2024","journal-title":"Journal of Beijing University of Posts and Telecommunications"},{"key":"e_1_2_11_10_2","first-page":"171","article-title":"PCB Defect Detection Algorithm Based on Improved YOLOv4","volume":"42","author":"Wu J. G.","year":"2021","journal-title":"Chinese Journal of Scientific Instrument"},{"key":"e_1_2_11_11_2","article-title":"PCB Bare Board Defect Detection Based on Adaptive Weighted Feature Fusion","volume":"36","author":"Qian W. M.","year":"2022","journal-title":"Journal of Electronic Measurement and Instrumentation"},{"key":"e_1_2_11_12_2","first-page":"235","article-title":"PCB Defect Detection Algorithm Based on YOLOv5","volume":"5","author":"Wang Z. Q.","year":"2024","journal-title":"Engineering Management"},{"key":"e_1_2_11_13_2","first-page":"1","article-title":"Deep PCB Defect Detection Algorithm Based on Improved YOLOv5s","volume":"40","author":"Xie X.","year":"2023","journal-title":"Microelectronics & Computer"},{"key":"e_1_2_11_14_2","first-page":"11","article-title":"Accuracy Improvement of Deep Learning Algorithm for PCB Defect Detection","volume":"37","author":"Yang Y. Y.","year":"2023","journal-title":"Journal of Electronic Measurement and Instrumentation"},{"key":"e_1_2_11_15_2","first-page":"1","article-title":"PCB Defect Detection Algorithm Based on Lightweight YOLOv8n Network","volume":"47","author":"Li Z. K.","year":"2024","journal-title":"Electronic Measurement Technology"},{"key":"e_1_2_11_16_2","unstructured":"KhanamR.andHussainM. YOLOv11: An Overview of the Key Architectural Enhancements 2024 https:\/\/arxiv.org\/abs\/2410.17725."},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108837"},{"key":"e_1_2_11_18_2","unstructured":"XiaZ. PanX. SongS.et al. Vision Transformer with Deformable Attention Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2022 New Orleans LA IEEE 4794\u20134803."},{"key":"e_1_2_11_19_2","unstructured":"YangG. Y. LiuS. HuangD.et al. AFPN: Asymptotic Feature Pyramid Network for Object Detection 2023 https:\/\/arxiv.org\/abs\/2306.15988."},{"key":"e_1_2_11_20_2","doi-asserted-by":"crossref","unstructured":"DaiJ. QiH. XiongY.et al. Deformable Convolutional Networks Proceedings of the 2017 IEEE International Conference on Computer Vision 2017 Venice IEEE 764\u2013773.","DOI":"10.1109\/ICCV.2017.89"},{"key":"e_1_2_11_21_2","article-title":"Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN","volume":"236","author":"Wai K. L.","year":"2024","journal-title":"Expert Systems With Applications"},{"key":"e_1_2_11_22_2","unstructured":"LiuS. T. HuangD. andWangY. H. Learning Spatial Fusion for Single-Shot Object Detection 2019 https:\/\/arxiv.org\/abs\/1911.09516."},{"key":"e_1_2_11_23_2","first-page":"114","article-title":"PCB Surface Defect Detection System Based on Improved YOLOv5","author":"Xu P. H.","year":"2024","journal-title":"Information Technology and Informatization"},{"key":"e_1_2_11_24_2","first-page":"1","article-title":"PCB Surface Defect Detection Algorithm Based on PED-YOLOv11n","volume":"34","author":"Yang J. 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