{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:00:03Z","timestamp":1782316803012,"version":"3.54.5"},"reference-count":26,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62162026"],"award-info":[{"award-number":["62162026"]}]},{"name":"National Natural Science Foundation of China","award":["20242BAB26019"],"award-info":[{"award-number":["20242BAB26019"]}]},{"name":"National Natural Science Foundation of China","award":["2022XWH115"],"award-info":[{"award-number":["2022XWH115"]}]},{"name":"National Natural Science Foundation of China","award":["2023C01GX019"],"award-info":[{"award-number":["2023C01GX019"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["62162026"],"award-info":[{"award-number":["62162026"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["20242BAB26019"],"award-info":[{"award-number":["20242BAB26019"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["2022XWH115"],"award-info":[{"award-number":["2022XWH115"]}]},{"name":"Jiangxi Provincial Natural Science Foundation","award":["2023C01GX019"],"award-info":[{"award-number":["2023C01GX019"]}]},{"name":"Special Project for Cultural Research of Henan Xing Culture Engineering","award":["62162026"],"award-info":[{"award-number":["62162026"]}]},{"name":"Special Project for Cultural Research of Henan Xing Culture Engineering","award":["20242BAB26019"],"award-info":[{"award-number":["20242BAB26019"]}]},{"name":"Special Project for Cultural Research of Henan Xing Culture Engineering","award":["2022XWH115"],"award-info":[{"award-number":["2022XWH115"]}]},{"name":"Special Project for Cultural Research of Henan Xing Culture Engineering","award":["2023C01GX019"],"award-info":[{"award-number":["2023C01GX019"]}]},{"name":"Anyang City Science and Technology Research Project","award":["62162026"],"award-info":[{"award-number":["62162026"]}]},{"name":"Anyang City Science and Technology Research Project","award":["20242BAB26019"],"award-info":[{"award-number":["20242BAB26019"]}]},{"name":"Anyang City Science and Technology Research Project","award":["2022XWH115"],"award-info":[{"award-number":["2022XWH115"]}]},{"name":"Anyang City Science and Technology Research Project","award":["2023C01GX019"],"award-info":[{"award-number":["2023C01GX019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Tackling the widespread problems of inaccuracies, slow detection speed, and poor adaptability in small object defect detection on PCB circuits, this study suggests a lightweight printed circuit board surface defect identification algorithm, building upon an improved YOLOv8-PCB. This algorithm first introduces the C2f_SHSA attention mechanism in the backbone network, which unites the merits of channel attention and spatial attention, facilitating an efficient fusion of local and global features in a lightweight manner, thereby enhancing the model\u2019s identification preciseness for small defects. Subsequently, in the neck network, the C2f_IdentityFormer structure, which combines the C2f structure with the IdentityFormer structure, supplants the initial C2f structure. This enhancement improves the model\u2019s sensitivity to subtle features and further optimizes the effect of feature fusion. Eventually, the PIoU is presented to enhance the model\u2019s adaptability to small, complex PCB defects with varying sizes and shapes, while also accelerating the mode\u2019s convergence speed. Experimental outcomes reveal that the improved YOLOv8-PCB algorithm displays remarkable performance in the PCB dataset, with a Recall rate of 94.0%, a mean Average Precision (mAP) of 96.1%, and an F1 score of 94.35%. Moreover, the model\u2019s weight size is only 5.2 MB. Compared to the YOLOv8n baseline model, the Recall rate has a 3.6% improvement, the mAP is raised by 1.8%, and the F1 score is enhanced by 1.9%, while the model\u2019s weight is reduced by 17.46%. The enhancements in performance metrics confirm that the improved algorithm not only fulfills the requirements for efficient and real-time detection in PCB surface defect identification tasks but is also better suited for deployment and operation on edge devices.<\/jats:p>","DOI":"10.3390\/sym17020309","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T05:34:26Z","timestamp":1739943266000},"page":"309","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Lightweight PCB Defect Detection Algorithm Based on Improved YOLOv8-PCB"],"prefix":"10.3390","volume":"17","author":[{"given":"Jianan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Software Engineering, Anyang Normal University, Anyang 455000, China"},{"name":"Engineering Research Center for Intelligent Industry of Oracle Bone Culture in Henan Province, Anyang Normal University, Anyang 455000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoying","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Anyang Normal University, Anyang 455000, China"},{"name":"Engineering Research Center for Intelligent Industry of Oracle Bone Culture in Henan Province, Anyang Normal University, Anyang 455000, China"},{"name":"Key Laboratory of Oracle Bone Inscriptions Information Processing, Ministry of Education of China, Anyang 455000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Anyang Normal University, Anyang 455000, China"},{"name":"Engineering Research Center for Intelligent Industry of Oracle Bone Culture in Henan Province, Anyang Normal University, Anyang 455000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"key":"ref_1","first-page":"506","article-title":"Research on defect detection of lightweight PCB based on dual channel attention","volume":"35","author":"Peng","year":"2024","journal-title":"J. Optoelectron. Laser"},{"key":"ref_2","unstructured":"Tang, S., He, F., and Huang, X. (2019). Online PCB Defect Detector on A New PCB Defect Dataset. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chaudhary, V., Dave, I., and Upla, K. (2017, January 22\u201324). Automatic visual inspection of printed circuit board for defect detection and classification. Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India.","DOI":"10.1109\/WiSPNET.2017.8299858"},{"key":"ref_4","first-page":"57","article-title":"Research on PCB defect detection and classification based on machine learning","volume":"32","author":"Li","year":"2024","journal-title":"Print. Circuit Inf."},{"key":"ref_5","first-page":"136","article-title":"Improved LightweightX-Ray Aluminum Alloy Weld Defects Detection Algorithm Based on YOLOv5","volume":"49","author":"Cheng","year":"2022","journal-title":"Chin. J. Lasers"},{"key":"ref_6","first-page":"115","article-title":"Detection Method for OLED Pixel Defects Based on Extended Feature Pyramid","volume":"43","author":"Liu","year":"2023","journal-title":"Acta Opt. Sin."},{"key":"ref_7","first-page":"146","article-title":"Deep Transfer Learning-Based Pulsed Eddy Current Thermography for Crack Defect Detection","volume":"43","author":"Hao","year":"2023","journal-title":"Acta Opt. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1049\/trit.2019.0019","article-title":"TDD-net: A tiny defect detection network for printed circuit boards","volume":"4","author":"Ding","year":"2019","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_9","first-page":"366","article-title":"Single-shot detector with enriched semantics for PCB tiny defect detection","volume":"2020","author":"Shi","year":"2020","journal-title":"J. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lan, Z., Hong, Y., and Li, Y. (2021, January 22\u201324). An improved YOLOv3 method for PCB surface defect detection. Proceedings of the 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, China.","DOI":"10.1109\/ICPECA51329.2021.9362675"},{"key":"ref_11","first-page":"171","article-title":"A defect detection method for PCB based on the improved YOLOv4","volume":"42","author":"Wu","year":"2021","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"87617","DOI":"10.1109\/ACCESS.2022.3198994","article-title":"Printed Circuit Board Quality Detection Method Integrating Lightweight Network and Dual Attention Mechanism","volume":"10","author":"Wu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_13","first-page":"101","article-title":"Real-time PCB Fault Detection Algorithm Based on Darknet Network and YOLO4 Algorithm","volume":"31","author":"Zhao","year":"2023","journal-title":"Comput. Meas. Control"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lan, H., Zhu, H., Luo, R., Ren, Q., and Chen, C. (2023, January 27\u201329). PCB Defect Detection Algorithm of Improved YOLOv8. Proceedings of the 2023 8th International Conference on Image, Vision and Computing (ICIVC), Dalian, China.","DOI":"10.1109\/ICIVC58118.2023.10270049"},{"key":"ref_15","first-page":"142","article-title":"Improved YOLOv8s Model for Small Object Detection from Perspective of Drones","volume":"60","author":"Pan","year":"2024","journal-title":"Comput. Eng. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"125122","DOI":"10.1109\/ACCESS.2023.3330844","article-title":"DS-YOLOv8-Based Object Detection Method for Remote Sensing Images","volume":"11","author":"Shen","year":"2023","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yun, S., and Ro, Y. (2024, January 16\u201322). Shvit: Single-head vision transformer with memory efficient macro design. Proceedings of the IEEE CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00550"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8210","DOI":"10.1109\/TNNLS.2022.3144163","article-title":"MHSA-Net: Multihead self-attention network for occluded person re-identification","volume":"34","author":"Tan","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/TPAMI.2023.3329173","article-title":"MetaFormer Baselines for Vision","volume":"46","author":"Weihao","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.neunet.2023.11.041","article-title":"Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism","volume":"170","author":"Liu","year":"2024","journal-title":"Neural Netw."},{"key":"ref_22","first-page":"33","article-title":"Bare PCB Defect Detection Based on Improved YOLOv5 Algorithm","volume":"43","author":"Xu","year":"2022","journal-title":"Packag. Eng."},{"key":"ref_23","first-page":"112","article-title":"Printed circuit board defect detection based on improved YOLOv5","volume":"46","author":"Li","year":"2023","journal-title":"Electron. Meas. Technol."},{"key":"ref_24","first-page":"2791","article-title":"PCB Bare Board Defect Detection Based on Improved YOLOv7 Algorithm","volume":"53","author":"Zhou","year":"2023","journal-title":"Radio. Eng."},{"key":"ref_25","first-page":"0412002","article-title":"Defect detection of printed circuit boards based on YOLOv8-PCB","volume":"62","author":"Wang","year":"2025","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_26","first-page":"2479","article-title":"DCW-YOLOv8 Detection Algorithm for PCB Electronic Components","volume":"45","author":"Li","year":"2024","journal-title":"J. Chin. Comput. Syst."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/2\/309\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:37:33Z","timestamp":1760027853000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/2\/309"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,19]]},"references-count":26,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["sym17020309"],"URL":"https:\/\/doi.org\/10.3390\/sym17020309","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,19]]}}}