{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T14:04:54Z","timestamp":1764165894941,"version":"3.46.0"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taiyuan City Unveiling and Marshalling Scheme","award":["2024TYJB0106"],"award-info":[{"award-number":["2024TYJB0106"]}]},{"name":"Special Funding for Guiding the Transformation of Scientific and Technological Achievements in Shanxi Province","award":["202204021301059"],"award-info":[{"award-number":["202204021301059"]}]},{"name":"Shanxi Provincial Science and Technology Major Special Project \u2018Unveiling the List of Commanders\u2019","award":["202301020101001"],"award-info":[{"award-number":["202301020101001"]}]},{"name":"Special Fund for Science and Technology Innovation Teams of Shanxi Province","award":["202304051001004"],"award-info":[{"award-number":["202304051001004"]}]},{"name":"Major Science and Technology Project of Shanxi Province","award":["202201090301013"],"award-info":[{"award-number":["202201090301013"]}]},{"name":"Supported by Fundamental Research Program of Shanxi Province","award":["202303021222164"],"award-info":[{"award-number":["202303021222164"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>To improve the detection performance of small defects in photovoltaic modules, we propose an enhanced YOLOv11n model\u2014YOLO-FAD. Its core innovations include the following: (1) integrating RFAConv into the backbone network and neck network to better capture small defect features in complex backgrounds; (2) adding DyC3K2 for adaptive convolution optimization to improve accuracy and robustness; (3) employing ASF for multi-layer feature fusion, and combining it with DyHead-detect in the fourth detection layer to refine the classification and localization of small targets. Testing on our dataset shows that YOLO-FAD achieves an overall accuracy of 94.6% (85.3% for small defects), outperforming YOLOv11n by 3.0% and 10.1% in mAP, respectively, and surpassing YOLOv12, RT-DETR, Improved Faster-RCNN, and state-of-the-art (SOTA) improved models.<\/jats:p>","DOI":"10.3390\/computers14120518","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T13:56:34Z","timestamp":1764165394000},"page":"518","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Small Defect Recognition in PV Modules with YOLO-FAD and Dynamic Convolution"],"prefix":"10.3390","volume":"14","author":[{"given":"Lijuan","family":"Li","sequence":"first","affiliation":[{"name":"College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5769-0565","authenticated-orcid":false,"given":"Gang","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}]},{"given":"Yin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}]},{"given":"Wang","family":"Yun","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}]},{"given":"Jianan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}]},{"given":"Zhicheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4685","DOI":"10.1109\/TII.2023.3327572","article-title":"SSN: Shift suppression network for endogenous shift of photovoltaic defect detection","volume":"20","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/TII.2012.2209663","article-title":"Defect detection in solar modules using ICA basis images","volume":"9","author":"Tsai","year":"2012","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1109\/TII.2021.3088721","article-title":"Investigating the impact of cracks on solar cells performance: Analysis based on nonuniform and uniform crack distributions","volume":"18","author":"Dhimish","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.jsamd.2017.05.005","article-title":"The impact of cracks on photovoltaic power performance","volume":"2","author":"Dhimish","year":"2017","journal-title":"J. Sci. Adv. Mater. Devices"},{"key":"ref_5","first-page":"805325","article-title":"Detection and localization of defects in monocrystalline silicon solar cell","volume":"2010","author":"Grmela","year":"2010","journal-title":"Adv. Opt. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hu, H., Gu, J., Zhang, Z., Dai, J., and Wei, Y. (2018, January 18\u201323). Relation networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00378"},{"key":"ref_7","unstructured":"Li, Y., Chen, Y., Wang, N., and Zhang, Z.-X. (November, January 27). Scale-aware trident networks for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_9","unstructured":"Liu, S., Huang, D., and Wang, Y. (2019). Learning spatial fusion for single-shot object detection. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"116534","DOI":"10.1109\/ACCESS.2023.3325677","article-title":"UAV target detection algorithm based on improved YOLOv8","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1007\/s10586-023-04079-7","article-title":"YOLO-CEA: A real-time industrial defect detection method based on contextual enhancement and attention","volume":"27","author":"Zhao","year":"2024","journal-title":"Clust. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"015405","DOI":"10.1088\/1361-6501\/acfe2f","article-title":"Adaptive receptive field based on multi-size convolution kernel for micro-defect detection of turbine blades","volume":"35","author":"Liu","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1007\/s11760-021-02010-4","article-title":"Multi-scale global context feature pyramid network for object detector","volume":"16","author":"Li","year":"2022","journal-title":"Signal Image Video Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, G., Luo, Z., Tian, Z., Zhang, Y., Zhang, W., and Wang, X. (2023, January 17\u201324). Towards efficient use of multi-scale features in transformer-based object detectors. Proceedings of the IEEE\/CVF Conference On Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00601"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_18","unstructured":"Zhang, X., Liu, C., Yang, D., Song, T., Ye, Y., Li, K., and Song, Y. (2023). RFAConv: Innovating spatial attention and standard convolutional operation. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105057","DOI":"10.1016\/j.imavis.2024.105057","article-title":"ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation","volume":"147","author":"Kang","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Guo, J., and Wu, E. (2024, January 16\u201322). ParameterNet: Parameters are all you need for large-scale visual pretraining of mobile networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01491"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., and Zhang, L. (2021, January 20\u201325). Dynamic head: Unifying object detection heads with attentions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00729"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, L.-H., Zhou, Y.-Z., Liu, L., Cao, W., and Ma, J.-H. (2025). Research on object detection and recognition in remote sensing images based on YOLOv11. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-96314-x"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sohan, M., Sai Ram, T., and Rami Reddy, C.V. (2024). A review on yolov8 and its advancements. Data Intelligence and Cognitive Informatics: Proceedings of the ICDICI 2023, Tirunelveli, India, 27\u201328 June 2023, Springer.","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Mark Liao, H.Y. (2025). Yolov9: Learning what you want to learn using programmable gradient information. Computer Vision: Proceedings of the 18th European Conference, Milan, Italy, 29 September\u20134 October 2024, Springer.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_25","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., and Han, J. (2024). Yolov10: Real-time end-to-end object detection. arXiv."},{"key":"ref_26","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). Yolov12: Attention-centric real-time object detectors. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 16\u201322). Detrs beat yolos on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"200193","DOI":"10.1016\/j.sasc.2025.200193","article-title":"Personalized Icon Design Model Based on Improved Faster-RCNN","volume":"7","author":"Wang","year":"2025","journal-title":"Syst. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101670","DOI":"10.1016\/j.jksuci.2023.101670","article-title":"BiTNet: A lightweight object detection network for real-time classroom behavior recognition with transformer and bi-directional pyramid network","volume":"35","author":"Zhao","year":"2023","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ren, S., Song, J., Yu, L., Tian, S., and Long, J. (2024). DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps Esophageal Cancer. Res. Sq.","DOI":"10.21203\/rs.3.rs-4074263\/v1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Feng, X., Gao, X., and Luo, L. (2021). X-SDD: A new benchmark for hot rolled steel strip surface defects detection. Symmetry, 13.","DOI":"10.3390\/sym13040706"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, J., and Zhang, S. (2025, January 21\u201323). Printed circuit board defect detection based on improved YOLOv5. Proceedings of the 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE), Shanghai, China.","DOI":"10.1109\/ICAACE65325.2025.11019637"},{"key":"ref_33","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2022). ELPV: Electroluminescence PV Module Dataset. arXiv."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/12\/518\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T14:01:33Z","timestamp":1764165693000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/12\/518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["computers14120518"],"URL":"https:\/\/doi.org\/10.3390\/computers14120518","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]}}}