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To detect faults of solar panels in large photovoltaic plants, drones with infrared cameras have been implemented. Drones may capture a huge number of infrared images. It is not realistic to manually analyze such a huge number of infrared images. To solve this problem, we develop a Deep Edge-Based Fault Detection (DEBFD) method, which applies convolutional neural networks (CNNs) for edge detection and object detection according to the captured infrared images. Particularly, a machine learning-based contour filter is designed to eliminate incorrect background contours. Then faults of solar panels are detected. Based on these fault detection results, solar panels can be classified into two classes, i.e., normal and faulty ones (i.e., macro ones). We collected 2060 images in multiple scenes and achieved a high macro F1 score. Our method achieved a frame rate of 28 fps over infrared images of solar panels on an NVIDIA GeForce RTX 2080 Ti GPU.<\/jats:p>","DOI":"10.3390\/s24165348","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T06:41:31Z","timestamp":1724049691000},"page":"5348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deep Edge-Based Fault Detection for Solar Panels"],"prefix":"10.3390","volume":"24","author":[{"given":"Haoyu","family":"Ling","sequence":"first","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2503-8817","authenticated-orcid":false,"given":"Manlu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,19]]},"reference":[{"key":"ref_1","unstructured":"Ram, M., Bogdanov, D., Aghahosseini, A., Gulagi, A., Oyewo, A.S., Child, M., Caldera, U., Sadovskaia, K., Farfan, J., and Barbosa, L.S.N.S. (2019). Global Energy System Based on 100% Renewable Energy\u2014Power, Heat, Transport and Desalination Sectors, Energy Watch Group."},{"key":"ref_2","first-page":"37","article-title":"Fault diagnosis of visual faults in photovoltaic modules: A Review","volume":"18","author":"Sugumaran","year":"2020","journal-title":"Int. J. Green Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.solener.2017.08.069","article-title":"A comprehensive study on different types of faults and detection techniques for solar photovoltaic system","volume":"158","author":"Madeti","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bhaskaranand, M., and Gibson, J.D. (2011, January 7\u201310). Low-complexity video encoding for UAV reconnaissance and surveillance. Proceedings of the 2011-MILCOM 2011 Military Communications Conference, Baltimore, MD, USA.","DOI":"10.1109\/MILCOM.2011.6127543"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Barbedo, J.G.A., Koenigkan, L.V., Santos, T.T., and Santos, P.M. (2019). A study on the detection of cattle in UAV images using deep learning. Sensors, 19.","DOI":"10.20944\/preprints201912.0089.v1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sa, I., Hrabar, S., and Corke, P. (2015). Outdoor flight testing of a pole inspection UAV incorporating high-speed vision. Field and Service Robotics, Springer.","DOI":"10.1007\/978-3-319-07488-7_8"},{"key":"ref_7","first-page":"1","article-title":"RCAG-Net: Residual Channelwise Attention Gate Network for Hot Spot Defect Detection of Photovoltaic Farms","volume":"70","author":"Su","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.solener.2020.01.055","article-title":"Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning","volume":"198","author":"Akram","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dotenco, S., Dalsass, M., Winkler, L., W\u00fcrzner, T., Brabec, C., Maier, A., and Gallwitz, F. (2016, January 7\u201310). Automatic detection and analysis of photovoltaic modules in aerial infrared imagery. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477658"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Vega D\u00edaz, J.J., Vlaminck, M., Lefkaditis, D., Orjuela Vargas, S.A., and Luong, H. (2020). Solar panel detection within complex backgrounds using thermal images acquired by UAVs. Sensors, 20.","DOI":"10.3390\/s20216219"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, Y., and Ling, Q. (2020, January 22\u201324). Hot-Spot Detection for Thermographic Images of Solar Panels. Proceedings of the 2020 Chinese Control and Decision Conference (CCDC), Hefei, China.","DOI":"10.1109\/CCDC49329.2020.9164255"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"40547","DOI":"10.1109\/ACCESS.2020.2976843","article-title":"Defects inspection in polycrystalline solar cells electroluminescence images using deep learning","volume":"8","author":"Rahman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","first-page":"1653","article-title":"A hybrid fuzzy convolutional neural network based mechanism for photovoltaic cell defect detection with electroluminescence images","volume":"32","author":"Ge","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lin, H.H., Dandage, H.K., Lin, K.M., Lin, Y.T., and Chen, Y.J. (2021). Efficient cell segmentation from electroluminescent images of single-crystalline silicon photovoltaic modules and cell-based defect identification using deep learning with pseudo-colorization. Sensors, 21.","DOI":"10.3390\/s21134292"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"4084","DOI":"10.1109\/TII.2020.3008021","article-title":"Deep learning-based solar-cell manufacturing defect detection with complementary attention network","volume":"17","author":"Su","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Mehta, S., Azad, A.P., Chemmengath, S.A., Raykar, V., and Kalyanaraman, S. (2018, January 12\u201315). Deepsolareye: Power loss prediction and weakly supervised soiling localization via fully convolutional networks for solar panels. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00043"},{"key":"ref_19","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_22","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_23","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_24","unstructured":"Jocher, G. (2021, December 01). YOLOv5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","article-title":"Contour detection and hierarchical image segmentation","volume":"33","author":"Arbelaez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","first-page":"15","article-title":"Object enhancement and extraction","volume":"Volume 10","author":"Prewitt","year":"1970","journal-title":"Picture Processing and Psychopictorics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"6","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","first-page":"1558","article-title":"Fast edge detection using structured forests","volume":"37","author":"Zitnick","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hallman, S., and Fowlkes, C.C. (2015, January 7\u201312). Oriented edge forests for boundary detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298782"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/TPAMI.2004.1273918","article-title":"Learning to detect natural image boundaries using local brightness, color, and texture cues","volume":"26","author":"Martin","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xie, S., and Tu, Z. (2015, January 7\u201313). Holistically-nested edge detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.164"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhao, X., and Huang, K. (2017, January 21\u201326). Deep crisp boundaries. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.187"},{"key":"ref_33","unstructured":"Xu, D., Ouyang, W., Alameda-Pineda, X., Ricci, E., Wang, X., and Sebe, N. (2018). Learning deep structured multi-scale features using attention-gated crfs for contour prediction. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cheng, M.M., Hu, X., Wang, K., and Bai, X. (2017, January 21\u201326). Richer convolutional features for edge detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.622"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Deng, R., Shen, C., Liu, S., Wang, H., and Liu, X. (2018, January 8\u201314). Learning to predict crisp boundaries. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01231-1_35"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, J., Zhang, S., Yang, M., Shan, Y., and Huang, T. (2019, January 15\u201320). Bi-directional cascade network for perceptual edge detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00395"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Su, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., Pietik\u00e4inen, M., and Liu, L. (2021, January 11\u201317). Pixel difference networks for efficient edge detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00507"},{"key":"ref_38","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_39","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_40","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_41","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.Y., and Le, Q.V. (2019, January 15\u201320). Nas-fpn: Learning scalable feature pyramid architecture for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/0734-189X(85)90016-7","article-title":"Topological structural analysis of digitized binary images by border following","volume":"30","author":"Suzuki","year":"1985","journal-title":"Comput. Vision Graph. Image Process."},{"key":"ref_45","unstructured":"Schwarz, J., Teich, J., Welzl, E., and Evans, B. (1994). On Finding a Minimal Enclosing Parallelogram, International Computer Science Institute. Technical Report tr-94-036."},{"key":"ref_46","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5348\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:38:49Z","timestamp":1760110729000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5348"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,19]]},"references-count":46,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24165348"],"URL":"https:\/\/doi.org\/10.3390\/s24165348","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,19]]}}}