{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:34:37Z","timestamp":1784140477736,"version":"3.55.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100018540","name":"Natural Science Foundation for Distinguished Young Scholars of Guangdong Province","doi-asserted-by":"crossref","award":["2022B1515020002"],"award-info":[{"award-number":["2022B1515020002"]}],"id":[{"id":"10.13039\/501100018540","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Flexible circuit boards are a cornerstone of the modern electronics industry. In automatic defect detection, FPC connectors present challenges such as minimal differences between oxidation defects and the background, easy degradation of Intersection over Union (<jats:italic>IoU<\/jats:italic>) scores, and significant variations in the shapes of black defect boundaries. Consequently, existing algorithms perform poorly in this task. We improve model YOLOv9 by introducing Multi-scale Dilated Attention (MSDA) on the output side to enhance the ability to capture features, and Deformable Large Kernel Attention (DLKA) on the other side of the output header to improve the ability to adapt to complex defect boundaries. Our use of<jats:italic>IoU<\/jats:italic>loss completely eliminates the risk of<jats:italic>IoU<\/jats:italic>degradation or gradient vanishing. Furthermore, we reduce computational overhead with the implementation of Faster Block. Following these improvements, the mean Average Precision (mAP) at 75%<jats:italic>IoU<\/jats:italic>(mAP75) for oxidized defects increased by 7.5% relative to the base model. Similarly, the mAP at 50%<jats:italic>IoU<\/jats:italic>(mAP50) for black defects increased by 5.7%, validating the relevance and efficacy of our proposed improvements. Overall, the average mAP50, mAP75, and mAP50:95 for all defects improved by 3.8%, 2.0%, and 2.3%, respectively. The performance gain achieved by our enhanced model significantly exceeds the improvement of YOLOv9 relative to YOLOv8.<\/jats:p>","DOI":"10.1007\/s44196-024-00669-4","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T16:03:20Z","timestamp":1729526600000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["An Improved YOLOv9 and Its Applications for Detecting Flexible Circuit Boards Connectors"],"prefix":"10.1007","volume":"17","author":[{"given":"Gengjie","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinbing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziwen","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuecong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guidong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wendong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiran","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"669_CR1","doi-asserted-by":"publisher","unstructured":"Zang, Y., Zhang, J., Billah, M.M.: Defect detection of flexible circuit board based on convolutional neural network. In: The 2nd International Conference on Computing and Data Science. CONF-CDS 2021. Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3448734.3450927","DOI":"10.1145\/3448734.3450927"},{"issue":"12","key":"669_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0295400","volume":"18","author":"X Shen","year":"2023","unstructured":"Shen, X., Xing, Y., Lu, J., Yu, F.: Detection of surface defect on flexible printed circuit via guided box improvement in ga-faster-rcnn network. PLOS ONE 18(12), 1\u201312 (2023). https:\/\/doi.org\/10.1371\/journal.pone.0295400","journal-title":"PLOS ONE"},{"key":"669_CR3","doi-asserted-by":"publisher","unstructured":"Luo, W., Luo, J., Yang, Z.: Fpc surface defect detection based on improved faster r-cnn with decoupled rpn. In: 2020 Chinese Automation Congress (CAC), pp. 7035\u20137039 (2020). https:\/\/doi.org\/10.1109\/CAC51589.2020.9326862","DOI":"10.1109\/CAC51589.2020.9326862"},{"key":"669_CR4","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"669_CR5","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(6), 1137\u20131149 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"669_CR6","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"669_CR7","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. CoRR abs\/1612.08242 (2016) arxiv:1612.08242","DOI":"10.1109\/CVPR.2017.690"},{"key":"669_CR8","unstructured":"Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. CoRR abs\/1804.02767 (2018) arxiv:1804.02767"},{"key":"669_CR9","unstructured":"Bochkovskiy, A., Wang, C., Liao, H.M.: Yolov4: Optimal speed and accuracy of object detection. CoRR abs\/2004.10934 (2020) arxiv:2004.10934"},{"key":"669_CR10","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., Zhao, Q.: Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 2778\u20132788 (2021)","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"669_CR11","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors (2022)","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"669_CR12","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., Liao, H.-Y.M.: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information (2024)","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"669_CR13","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: YOLOv10: Real-Time End-to-End Object Detection (2024)"},{"key":"669_CR14","doi-asserted-by":"publisher","unstructured":"Adibhatla, V.A., Chih, H.-C., Hsu, C.-C., Cheng, J., Abbod, M.F., Shieh, J.-S.: Defect detection in printed circuit boards using you-only-look-once convolutional neural networks. Electronics 9(9) (2020) https:\/\/doi.org\/10.3390\/electronics9091547","DOI":"10.3390\/electronics9091547"},{"issue":"1","key":"669_CR15","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MMUL.2024.3359267","volume":"31","author":"X Wang","year":"2024","unstructured":"Wang, X., Zhang, H., Liu, Q., Gong, W., Bai, S., You, H.: You-only-look-once multiple-strategy printed circuit board defect detection model. IEEE MultiMedia 31(1), 76\u201387 (2024). https:\/\/doi.org\/10.1109\/MMUL.2024.3359267","journal-title":"IEEE MultiMedia"},{"issue":"1","key":"669_CR16","doi-asserted-by":"publisher","first-page":"9805","DOI":"10.1038\/s41598-023-36854-2","volume":"13","author":"K Xia","year":"2023","unstructured":"Xia, K., Lv, Z., Liu, K., Lu, Z., Zhou, C., Zhu, H., Chen, X.: Global contextual attention augmented yolo with convmixer prediction heads for pcb surface defect detection. Scientific reports 13(1), 9805 (2023)","journal-title":"Scientific reports"},{"key":"669_CR17","doi-asserted-by":"publisher","unstructured":"Li, J., Gu, J., Huang, Z., Wen, J.: Application research of improved yolo v3 algorithm in pcb electronic component detection. Applied Sciences 9(18) (2019) https:\/\/doi.org\/10.3390\/app9183750","DOI":"10.3390\/app9183750"},{"key":"669_CR18","doi-asserted-by":"publisher","unstructured":"Yuan, M., Zhou, Y., Ren, X., Zhi, H., Zhang, J., Chen, H.: Yolo-hmc: An improved method for pcb surface defect detection. IEEE Transactions on Instrumentation and Measurement 73, 1\u201311 (2024) https:\/\/doi.org\/10.1109\/TIM.2024.3351241","DOI":"10.1109\/TIM.2024.3351241"},{"key":"669_CR19","doi-asserted-by":"publisher","unstructured":"Chen, W., Huang, Z., Mu, Q., Sun, Y.: Pcb defect detection method based on transformer-yolo. IEEE Access 10, 129480\u2013129489 (2022) https:\/\/doi.org\/10.1109\/ACCESS.2022.3228206","DOI":"10.1109\/ACCESS.2022.3228206"},{"key":"669_CR20","unstructured":"Santoso, A.D., Cahyono, F.B., Prahasta, B., Sutrisno, I., Khumaidi, A.: Development of pcb defect detection system using image processing with yolo cnn method. International Journal of Artificial Intelligence Research 6(1) (2022)"},{"key":"669_CR21","unstructured":"Mnih, V., Heess, N., Graves, A., et al.: Recurrent models of visual attention. Advances in neural information processing systems 27 (2014)"},{"key":"669_CR22","doi-asserted-by":"publisher","unstructured":"Hu, J., Zhi, X., Shi, T., Zhang, W., Cui, Y., Zhao, S.: Pag-yolo: A portable attention-guided yolo network for small ship detection. Remote Sensing 13(16) (2021) https:\/\/doi.org\/10.3390\/rs13163059","DOI":"10.3390\/rs13163059"},{"key":"669_CR23","doi-asserted-by":"crossref","unstructured":"Xue, Y., Ju, Z., Li, Y., Zhang, W.: Maf-yolo: Multi-modal attention fusion based yolo for pedestrian detection. Infrared Physics & Technology 118, 103906 (2021)","DOI":"10.1016\/j.infrared.2021.103906"},{"key":"669_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Lu, X., Cao, G., Yang, Y., Jiao, L., Liu, F.: Vit-yolo:transformer-based yolo for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 2799\u20132808 (2021)","DOI":"10.1109\/ICCVW54120.2021.00314"},{"key":"669_CR25","doi-asserted-by":"publisher","unstructured":"Guo, Z., Wang, C., Yang, G., Huang, Z., Li, G.: Msft-yolo: Improved yolov5 based on transformer for detecting defects of steel surface. Sensors 22(9) (2022) https:\/\/doi.org\/10.3390\/s22093467","DOI":"10.3390\/s22093467"},{"key":"669_CR26","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00075"},{"issue":"07","key":"669_CR27","doi-asserted-by":"publisher","first-page":"12993","DOI":"10.1609\/aaai.v34i07.6999","volume":"34","author":"Z Zheng","year":"2020","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-iou loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence 34(07), 12993\u201313000 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.6999","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"8","key":"669_CR28","doi-asserted-by":"publisher","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","volume":"52","author":"Z Zheng","year":"2022","unstructured":"Zheng, Z., Wang, P., Ren, D., Liu, W., Ye, R., Hu, Q., Zuo, W.: Enhancing geometric factors in model learning and inference for object detection and instance segmentation. IEEE Transactions on Cybernetics 52(8), 8574\u20138586 (2022). https:\/\/doi.org\/10.1109\/TCYB.2021.3095305","journal-title":"IEEE Transactions on Cybernetics"},{"key":"669_CR29","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","volume":"506","author":"Y-F Zhang","year":"2022","unstructured":"Zhang, Y.-F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient iou loss for accurate bounding box regression. Neurocomputing 506, 146\u2013157 (2022)","journal-title":"Neurocomputing"},{"key":"669_CR30","unstructured":"Ma, S., Xu, Y.: MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression (2023)"},{"key":"669_CR31","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.-h., He, H., Zhuo, W., Wen, S., Lee, C.-H., Chan, S.-H.G.: Run, don\u2019t walk: Chasing higher flops for faster neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12021\u201312031 (2023)","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"669_CR32","doi-asserted-by":"publisher","first-page":"8906","DOI":"10.1109\/TMM.2023.3243616","volume":"25","author":"J Jiao","year":"2023","unstructured":"Jiao, J., Tang, Y.-M., Lin, K.-Y., Gao, Y., Ma, A.J., Wang, Y., Zheng, W.-S.: Dilateformer: Multi-scale dilated transformer for visual recognition. IEEE Transactions on Multimedia 25, 8906\u20138919 (2023). https:\/\/doi.org\/10.1109\/TMM.2023.3243616","journal-title":"IEEE Transactions on Multimedia"},{"key":"669_CR33","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable Convolutional Networks (2017)","DOI":"10.1109\/ICCV.2017.89"},{"issue":"4","key":"669_CR34","first-page":"733","volume":"9","author":"M-H Guo","year":"2023","unstructured":"Guo, M.-H., Lu, C.-Z., Liu, Z.-N., Cheng, M.-M., Hu, S.-M.: Visual attention network. Computational Visual. Media 9(4), 733\u2013752 (2023)","journal-title":"Media"},{"key":"669_CR35","doi-asserted-by":"crossref","unstructured":"Azad, R., Niggemeier, L., H\u00fcttemann, M., Kazerouni, A., Aghdam, E.K., Velichko, Y., Bagci, U., Merhof, D.: Beyond self-attention: Deformable large kernel attention for medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1287\u20131297 (2024)","DOI":"10.1109\/WACV57701.2024.00132"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00669-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00669-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00669-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:02:48Z","timestamp":1732924968000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00669-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["669"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00669-4","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"14 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"261"}}