{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T15:53:41Z","timestamp":1792079621998,"version":"4.4.0"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:00:00Z","timestamp":1771632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanxi Province Basic Research Program","award":["202403021222112"],"award-info":[{"award-number":["202403021222112"]}]},{"name":"Excellent Doctoral Research Initiation Program of the 24-Institute Consortium at Shanxi Agricultural University","award":["6K245406008"],"award-info":[{"award-number":["6K245406008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Given the rising global demand for environmentally sustainable energy sources, solar photovoltaic (PV) power generation has emerged as a pivotal component of the energy transition. In PV systems, power conversion efficiency is degraded and operational lifespan reduced due to the presence of defective modules. Consequently, achieving accurate and efficient defect detection during PV module manufacturing is critical to ensuring product quality and reliability. To address this challenge, we propose MambaVSS-YOLOv11n, an electroluminescence (EL) image-based multi-defect detection method for PV modules. Our study utilizes a dataset containing six types of defects\u2014Broken Gate, Cold Solder Joint, Black Spot, Scratch, Microcrack, and Suction Mark\u2014to construct 692 labeled EL images of defective PV modules. The model integrates the Vision State Space (VSS) module from Mamba and optimizes the C3k2 Bottleneck structure to enhance fine-grained feature extraction, while employing Space-to-Depth Convolutional (SPD-Conv) Layer for downsampling to improve computational efficiency. Additionally, to address YOLOv11n\u2019s limited generalization capability for small objects and complex backgrounds, we adopt the Inner Mask Distance Penalized Intersection over the Union (Inner-MDPIoU) loss function, which enhances detection accuracy and mitigates the impact of low-quality samples. Experimental results demonstrate that compared to YOLOv11n, MambaVSS-YOLOv11n reduces the number of parameters by 18.1%, while improving mAP@0.5 to 0.869 and mAP@0.5:0.95 to 0.637. This achieves model lightweighting while enhancing detection performance. These findings indicate that the model is well-suited for real-time defect detection in PV module production lines, providing PV manufacturers with a lightweight yet accurate and reliable solution for PV module defect inspection.<\/jats:p>","DOI":"10.3390\/s26041373","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T08:58:21Z","timestamp":1771837101000},"page":"1373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["MambaVSS-YOLOv11n: State Space Model-Enhanced Multi-Defect Detection in Photovoltaic Module Electroluminescence Images"],"prefix":"10.3390","volume":"26","author":[{"given":"Kun","family":"Wang","sequence":"first","affiliation":[{"name":"Software College, Shanxi Agricultural University, Jinzhong 030810, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixin","family":"Tang","sequence":"additional","affiliation":[{"name":"Software College, Shanxi Agricultural University, Jinzhong 030810, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"Software College, Shanxi Agricultural University, Jinzhong 030810, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Yang","sequence":"additional","affiliation":[{"name":"Software College, Shanxi Agricultural University, Jinzhong 030810, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Han","sequence":"additional","affiliation":[{"name":"Software College, Shanxi Agricultural University, Jinzhong 030810, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuzhong","family":"Li","sequence":"additional","affiliation":[{"name":"Software College, Shanxi Agricultural University, Jinzhong 030810, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guozhu","family":"Song","sequence":"additional","affiliation":[{"name":"Software College, Shanxi Agricultural University, Jinzhong 030810, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1093\/ce\/zkac023","article-title":"Solar energy technology and its roles in sustainable development","volume":"6","author":"Maka","year":"2022","journal-title":"Clean Energy"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kabeyi, M.J.B., and Olanrewaju, O.A. (2022). Sustainable energy transition for renewable and low carbon grid electricity generation and supply. Front. Energy Res., 9.","DOI":"10.3389\/fenrg.2021.743114"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.rser.2014.08.046","article-title":"Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation","volume":"41","author":"Hosenuzzaman","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_4","unstructured":"Vision VNES (2025, November 17). Towards 100% renewable energy by 2050, 2023. [Online]. Available online: https:\/\/wwfasia.awsassets.panda.org\/downloads\/-en--scenario-report_100re-map.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.rser.2018.11.012","article-title":"Factors influencing the efficiency of photovoltaic system","volume":"101","author":"Venkateswari","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"062701","DOI":"10.1088\/1674-4926\/41\/6\/062701","article-title":"Numerical study of mono-crystalline silicon solar cells with passivated emitter and rear contact configuration for the efficiency beyond 24% based on mass production technology","volume":"41","author":"Wang","year":"2020","journal-title":"J. Semicond."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111022","DOI":"10.1016\/j.rser.2021.111022","article-title":"Stress and strain within photovoltaic modules using the finite element method: A critical review","volume":"145","author":"Nivelle","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9651","DOI":"10.1109\/JIOT.2020.2983723","article-title":"Edge-computing-enabled unmanned module defect detection and diagnosis system for large-scale photovoltaic plants","volume":"7","author":"Li","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.solener.2019.02.067","article-title":"Automatic classification of defective photovoltaic module cells in electroluminescence images","volume":"185","author":"Deitsch","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"118822","DOI":"10.1016\/j.apenergy.2022.118822","article-title":"Failures of photovoltaic modules and their detection: A review","volume":"313","author":"Akram","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bansal, M.A., Sharma, D.R., and Kathuria, D.M. (2022). A systematic review on data scarcity problem in deep learning: Solution and applications. ACM Comput. Surv., 54.","DOI":"10.1145\/3502287"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hao, X., Liu, L., Yang, R., Yin, L., Zhang, L., and Li, X. (2023). A review of data augmentation methods of remote sensing image target recognition. Remote Sens., 15.","DOI":"10.3390\/rs15030827"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"114315","DOI":"10.1016\/j.enconman.2021.114315","article-title":"Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images","volume":"241","author":"Manno","year":"2021","journal-title":"Energy Convers. Manag."},{"key":"ref_14","unstructured":"Hoskere, V.A. (2020). Autonomy in Structural Inspections Through Computer Vision and Graphics. [Ph.D. Thesis, University of Illinois at Urbana-Champaign]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112353","DOI":"10.1016\/j.rser.2022.112353","article-title":"Inspection and condition monitoring of large-scale photovoltaic power plants: A review of imaging technologies","volume":"161","author":"Grujic","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"126162","DOI":"10.1016\/j.conbuildmat.2021.126162","article-title":"A critical review and comparative study on image segmentation-based techniques for pavement crack detection","volume":"321","author":"Kheradmandi","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_17","first-page":"56","article-title":"Non-destructive techniques for corrosion detection: A review, Corrosion Engineering","volume":"59","author":"Vasagar","year":"2024","journal-title":"Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112160","DOI":"10.1016\/j.rser.2022.112160","article-title":"Review of degradation and failure phenomena in photovoltaic modules","volume":"159","author":"Aghaei","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, Q., and Castleman, K.R. (2023). Image segmentation. Microscope Image Processing, Academic Press.","DOI":"10.1016\/B978-0-12-821049-9.00003-4"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bertocci, F., Grandoni, A., and Djuric-Rissner, T. (2019). Scanning acoustic microscopy (sam): A robust method for defect detection during the manufacturing process of ultrasound probes for medical imaging. Sensors, 19.","DOI":"10.3390\/s19224868"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.solener.2021.05.032","article-title":"Infrared thermography-based condition monitoring of solar photovoltaic systems: A mini review of recent advances","volume":"223","author":"Kandeal","year":"2021","journal-title":"Sol. Energy"},{"key":"ref_22","first-page":"107984","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_23","unstructured":"Hussain, M. (2024). Yolov5, yolov8 and yolov10: The go-to detectors for real-time vision. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e18826","DOI":"10.1016\/j.heliyon.2023.e18826","article-title":"Solar panel defect detection design based on yolo v5 algorithm","volume":"9","author":"Huang","year":"2023","journal-title":"Heliyon"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, N., Huang, S., Liu, X., Wang, Z., Liu, Y., and Gao, Z. (2025). Mra-yolov8: A network enhancing feature extraction ability for photovoltaic cell defects. Sensors, 25.","DOI":"10.3390\/s25051542"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Khanam, R., Asghar, T., and Hussain, M. (2025). Comparative performance evaluation of yolov5, yolov8, and yolov11 for solar panel defect detection. Solar, 5.","DOI":"10.20944\/preprints202501.0788.v1"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3278","DOI":"10.1038\/s41598-025-28603-4","article-title":"Multimodal fault detection model for photovoltaic modules","volume":"16","author":"Yu","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"127","DOI":"10.5194\/isprs-archives-XLVIII-2-W11-2025-127-2025","article-title":"Real time solar panels heat point\u2019s detection with EDGE ViTs using thermal drone imagery","volume":"XLVIII-2\/W11-2025","author":"Jabrane","year":"2025","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"114005","DOI":"10.1016\/j.solmat.2025.114005","article-title":"LWMF-DETR: A lightweight transformer network for photovoltaic hot-spot fault detection with multi-scale wavelet feature fusion","volume":"295","author":"Cao","year":"2026","journal-title":"Sol. Energy Mater. Sol. Cells"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhou, X., Li, X., Huang, W., and Wei, R. (2024). Lem-detector: An efficient detector for photovoltaic panel defect detection. Appl. Sci., 14.","DOI":"10.3390\/app142210290"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, X., Gao, L., Peng, Y., and Yang, X. (2023). Defect detection of photovoltaic panel electroluminescence image based on yolov7-se-ds-nwd algorithm. Proceedings of the 2023 3rd International Conference on Robotics, Automation and Intelligent Control (ICRAIC), IEEE.","DOI":"10.1109\/ICRAIC61978.2023.00029"},{"key":"ref_32","unstructured":"Gu, A., and Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv."},{"key":"ref_33","unstructured":"Qu, H., Ning, L., An, R., Fan, W., Derr, T., Liu, H., Xu, X., and Li, Q. (2024). A survey of mamba. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhu, Y., Wang, D., Zhang, L., Chen, T., Wang, Z., and Ye, Z. (2024). A survey on visual mamba. Appl. Sci., 14.","DOI":"10.3390\/app14135683"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhao, W., Liu, C., Yang, H., and Xu, W. (2024). Real-time object detection based on mamba and yolov8. 2024 4th International Conference on Industrial Automation, Robotics and Control Engineering (IARCE), IEEE.","DOI":"10.1109\/IARCE64300.2024.00055"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bai, M., Di, X., Yu, L., Ding, J., and Lin, H. (2025). A pine wilt disease detection model integrated with mamba model and attention mechanisms using uav imagery. Remote Sens., 17.","DOI":"10.3390\/rs17020255"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105770","DOI":"10.1016\/j.autcon.2024.105770","article-title":"Enhancing pixel-level crack segmentation with visual mamba and convolutional networks","volume":"168","author":"Han","year":"2024","journal-title":"Autom. Constr."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8793","DOI":"10.1007\/s11760-024-03507-4","article-title":"Yolo-mamba: Object detection method for infrared aerial images","volume":"18","author":"Zhao","year":"2024","journal-title":"Signal Image Video Process."},{"key":"ref_39","unstructured":"Zhao, X., Lv, C., Wang, Y., Wang, J., and Wei, Q. (2024). Mamba-YOLO: Efficient Remote Sensing Object Detection with Visual State Space Model. arXiv."},{"key":"ref_40","unstructured":"Glenn, J., and Qiu, J. (2024). Ultralytics YOLO11, Version 11.0.0, Ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_41","unstructured":"Liu, Y., Tian, Y., Zhao, Y., Yu, H., Xie, L., Wang, Y., Ye, Q., Jiao, J., and Liu, Y. (2024). VMamba: Visual state space model. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sunkara, R., and Luo, T. (2022). No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-031-26409-2_27"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1373\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T09:04:49Z","timestamp":1771837489000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/4\/1373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26041373"],"URL":"https:\/\/doi.org\/10.3390\/s26041373","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}