{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:47:30Z","timestamp":1782780450400,"version":"3.54.5"},"reference-count":22,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Imec","award":["HBC.2019.0050"],"award-info":[{"award-number":["HBC.2019.0050"]}]},{"name":"COMP4DRONES ECSEL Joint Undertaking (JU)","award":["826610"],"award-info":[{"award-number":["826610"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Today, solar energy is taking an increasing share of the total energy mix. Unfortunately, many operational photovoltaic plants suffer from a plenitude of defects resulting in non-negligible power loss. The latter highly impacts the overall performance of the PV site; therefore, operators need to regularly inspect their solar parks for anomalies in order to prevent severe performance drops. As this operation is naturally labor-intensive and costly, we present in this paper a novel system for improved PV diagnostics using drone-based imagery. Our solution consists of three main steps. The first step locates the solar panels within the image. The second step detects the anomalies within the solar panels. The final step identifies the root cause of the anomaly. In this paper, we mainly focus on the second step comprising the detection of anomalies within solar panels, which is done using a region-based convolutional neural network (CNN). Experiments on six different PV sites with different specifications and a variety of defects demonstrate that our anomaly detector achieves a true positive rate or recall of more than 90% for a false positive rate of around 2% to 3% tested on a dataset containing nearly 9000 solar panels. Compared to the best state-of-the-art methods, the experiments revealed that we achieve a slightly higher true positive rate for a substantially lower false positive rate, while tested on a more realistic dataset.<\/jats:p>","DOI":"10.3390\/s22031244","type":"journal-article","created":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T20:36:42Z","timestamp":1644266202000},"page":"1244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Region-Based CNN for Anomaly Detection in PV Power Plants Using Aerial Imagery"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3986-823X","authenticated-orcid":false,"given":"Michiel","family":"Vlaminck","sequence":"first","affiliation":[{"name":"IPI-URC-imec, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rugen","family":"Heidbuchel","sequence":"additional","affiliation":[{"name":"Sitemark, Gaston Geenslaan 11, 3001 Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wilfried","family":"Philips","sequence":"additional","affiliation":[{"name":"IPI-URC-imec, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6246-5538","authenticated-orcid":false,"given":"Hiep","family":"Luong","sequence":"additional","affiliation":[{"name":"IPI-URC-imec, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dimitrievska, V., Pittino, F., Muehleisen, W., Diewald, N., Hilweg, M., Montvay, A., and Hirschl, C. (2021). Statistical Methods for Degradation Estimation and Anomaly Detection in Photovoltaic Plants. Sensors, 21.","DOI":"10.3390\/s21113733"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Akiyama, Y., Kasai, Y., Iwata, M., Takahashi, E., Sato, F., and Murakawa, M. (2015, January 24\u201327). Anomaly Detection of Solar Power Generation Systems Based on the Normalization of the Amount of Generated Electricity. Proceedings of the 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, Gwangju, Korea.","DOI":"10.1109\/AINA.2015.198"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dubey, R., Chattopadhyay, S., Zachariah, S., Rambabu, S., Singh, H.K., Kottantharayil, A., Arora, B.M., Narasimhan, K., Shiradkar, N., and Vasi, J. (2018, January 10\u201315). On-Site Electroluminescence Study of Field-Aged PV Modules. Proceedings of the 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC 34th EU PVSEC), Waikoloa, HI, USA.","DOI":"10.1109\/PVSC.2018.8548080"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"117930","DOI":"10.1016\/j.energy.2020.117930","article-title":"Nondestructive characterization of solar PV cells defects by means of electroluminescence, infrared thermography, I-V curves and visual tests: Experimental study and comparison","volume":"205","year":"2020","journal-title":"Energy"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rogotis, S., Ioannidis, D., Tsolakis, A., Tzovaras, D., and Likothanassis, S. (2014, January 7\u201311). Early defect diagnosis in installed PV modules exploiting spatio-temporal information from thermal images. Proceedings of the 12th Quantitative InfraRed Thermography Conference (QIRT), Bordeaux, France.","DOI":"10.21611\/qirt.2014.038"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gao, X., Munson, E., Abousleman, G., and Si, J. (2015, January 20\u201324). Automatic solar panel recognition and defect detection using infrared imaging. Proceedings of the Automatic Target Recognition XXV, Baltimore, MD, USA.","DOI":"10.1117\/12.2179792"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Buerhop, C., Pickel, T., Dalsass, M., Scheuerpflug, H., Camus, C., and Brabec, C.J. (2016, January 5\u201310). aIR-PV-check: A quality inspection of PV-power plants without operation interruption. Proceedings of the 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), Portland, OR, USA.","DOI":"10.1109\/PVSC.2016.7749909"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1080\/14786451.2013.826223","article-title":"Fault Diagnosis of Photovoltaic Modules through Image Processing and Canny Edge Detection on Field Thermographic Measurements","volume":"34","author":"Tsanakas","year":"2015","journal-title":"Int. J. Sustain. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"179","DOI":"10.5194\/isprs-archives-XLII-2-W6-179-2017","article-title":"Automatic Fault Recogntion of Photovoltaic Modules based on Statistical Analysis of UAV Thermography","volume":"42","author":"Kim","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"893","DOI":"10.5194\/isprs-archives-XLII-2-893-2018","article-title":"Deep Convolutional Neural Network For Automatic Detection of Damaged Photovoltaic Cells","volume":"42","author":"Pierdicca","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pierdicca, R., Paolanti, M., Felicetti, A., Piccinini, F., and Zingaretti, P. (2020). Automatic Faults Detection of Photovoltaic Farms: SolAIr, a Deep Learning-Based System for Thermal Images. Energies, 13.","DOI":"10.3390\/en13246496"},{"key":"ref_12","unstructured":"de Oliveira, A.K.V., Aghaei, M., and R\u00fcther, R. (2019, January 9\u201313). Automatic Fault Detection of Photovoltaic Array by Convolutional Neural Networks During Aerial Infrared Thermography. Proceedings of the 36th European Photovoltaic Solar Energy Conference and Exhibition, Marseille, France."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.renene.2021.07.070","article-title":"Automatic fault classification in photovoltaic modules using Convolutional Neural Networks","volume":"179","author":"Marra","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Luzardo, G., Vlaminck, M., Lefkaditis, D., and Luong, H. (2021, January 12\u201316). GPS-Assisted Feature Matching in Aerial Images with Highly Repetitive Patterns. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553147"},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R.B. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV\u201915, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S.J. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5801870","DOI":"10.1155\/2020\/5801870","article-title":"Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks","volume":"2020","author":"Mateen","year":"2020","journal-title":"Complexity"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1244\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:15:15Z","timestamp":1760134515000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,7]]},"references-count":22,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22031244"],"URL":"https:\/\/doi.org\/10.3390\/s22031244","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,7]]}}}