{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:30:46Z","timestamp":1793129446293,"version":"4.5.0"},"reference-count":65,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"F\u0131rat University Scientific Research Projects Unit","award":["TEKF.25.56"],"award-info":[{"award-number":["TEKF.25.56"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study addresses the automatic classification of cells in electroluminescent panel images to detect photovoltaic cell defects. The images used in the study were obtained from a solar panel production line. An original dataset consisting of 37,538 cell images with eight defect classes (Cell-Interconnection, Electrically Insulated Cell Parts, Finger Defect, Material, Microcrack, Multi-Defect, Normal, Visual) was prepared by applying RLSA-based automated cell segmentation enhanced with morphological processing to the photovoltaic panel images. A novel CNN\u2013Transformer model with a self-attention mechanism, called PVELNet, is proposed for classifying defect types. Experimental studies were conducted with 16 deep learning models to compare the proposed model. F1-Score, Precision, Recall, and Accuracy evaluation metrics were used in the experimental study. Furthermore, the Confusion Matrix results obtained from the 16 deep learning models and the proposed PVELNet model are presented. The results were obtained using a relatively balanced dataset prepared for this study. PVELNet achieved 95.71% accuracy, outperforming other models. With 1.79 million parameters and a memory requirement of 46.1 MB, the PVELNet model is relatively lightweight. As a result, it demonstrates the potential to control processes on actual solar panel production lines.<\/jats:p>","DOI":"10.3390\/s26082450","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T11:35:42Z","timestamp":1776339342000},"page":"2450","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Hybrid CNN\u2013Transformer Approach for Photovoltaic Cell Defect Classification Using Electroluminescence Imaging"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0731-5668","authenticated-orcid":false,"given":"Miktat","family":"Akta\u015f","sequence":"first","affiliation":[{"name":"Software Engineering, GTC Gunes Sanayi ve Ticaret AS, Adiyaman 02040, T\u00fcrkiye"},{"name":"Software Engineering, Graduate School of Sciences, F\u0131rat University, Elaz\u0131\u011f 23119, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9203-697X","authenticated-orcid":false,"given":"Ferdi","family":"Do\u011fan","sequence":"additional","affiliation":[{"name":"Computer Engineering, Faculty of Engineering, Adiyaman University, Adiyaman 02040, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4938-4167","authenticated-orcid":false,"given":"\u0130brahim","family":"T\u00fcrko\u011flu","sequence":"additional","affiliation":[{"name":"Software Engineering, Faculty of Technology, F\u0131rat University, Elaz\u0131\u011f 23119, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/s10644-024-09641-w","article-title":"The role of solar energy in achieving net-zero emission and green growth: A global analysis","volume":"57","author":"Sun","year":"2024","journal-title":"Econ. Change Restruct."},{"key":"ref_2","unstructured":"Yadav, A.S., and Agrawal, V. (2018). Renewable Energy Sources: An Application Guide, LAP LAMBERT Academic Publishing."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Karaa\u011fa\u00e7, M.O., Erg\u00fcn, A., Arslan, O., and Kayfeci, M. (2023). Introduction to solar panels. Handbook of Thermal Management Systems, Elsevier.","DOI":"10.1016\/B978-0-443-19017-9.00011-8"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/pip.941","article-title":"Detection of the voltage distribution in photovoltaic modules by electroluminescence imaging","volume":"18","author":"Potthoff","year":"2010","journal-title":"Prog. Photovolt. Res. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"381","DOI":"10.24425\/mms.2023.146426","article-title":"Imaging methods of detecting defects in photovoltaic solar cells and modules: A survey","volume":"30","author":"Maziuk","year":"2023","journal-title":"Metrol. Meas. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111994","DOI":"10.1016\/j.solener.2023.111994","article-title":"Progress in module level quantitative electroluminescence imaging of crystalline silicon PV module: A review","volume":"264","author":"Puranik","year":"2023","journal-title":"Sol. Energy"},{"key":"ref_7","unstructured":"Jahn, U., Herz, M., K\u00f6ntges, M., Parlevliet, D., Paggi, M., Tsanakas, I., Stein, J., Berger, K.A., Ranta, S., and French, R.H. (2018). Review on Infrared and Electroluminescence Imaging for PV Field Applications: International Energy Agency Photovoltaic Power Systems Programme, International Energy Agency."},{"key":"ref_8","first-page":"10","article-title":"Comparison of Leaf Classification Performance of Deep Learning Algorithms","volume":"1","year":"2018","journal-title":"Sak. Univ. J. Comput. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1109\/JPHOTOV.2019.2920732","article-title":"Automated pipeline for photovoltaic module electroluminescence image processing and degradation feature classification","volume":"9","author":"Karimi","year":"2019","journal-title":"IEEE J. Photovolt."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Kaligambe, A., and Fujita, G. (2023). A deep learning-based framework for automatic detection of defective solar photovoltaic cells in electroluminescence images using transfer learning. Proceedings of the 2023 4th International Conference on High Voltage Engineering and Power Systems (ICHVEPS), IEEE.","DOI":"10.1109\/ICHVEPS58902.2023.10257399"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Grisanti, M., Spatafora, M.A.N., Ortis, A., and Battiato, S. (2023). E-ELPV: Extended ELPV dataset for accurate solar cells defect classification. Proceedings of the Proceedings of SAI Intelligent Systems Conference, Springer.","DOI":"10.1007\/978-3-031-47721-8_55"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Deitsch, S., Buerhop-Lutz, C., Sovetkin, E., Steland, A., Maier, A., Gallwitz, F., and Riess, C. (2018). Segmentation of photovoltaic module cells in electroluminescence images. arXiv.","DOI":"10.1016\/j.solener.2019.02.067"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, P., Bahadir Kocer, B., and Kovac, M. (2024). Drone-Based Solar Cell Inspection With Autonomous Deep Learning. Infrastructure Robotics: Methodologies, Robotic Systems and Applications, IEEE.","DOI":"10.1002\/9781394162871.ch16"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"409","DOI":"10.24012\/dumf.411130","article-title":"Derin \u00f6\u011frenme modelleri ve uygulama alanlar\u0131na ili\u015fkin bir derleme","volume":"10","year":"2019","journal-title":"Dicle \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi M\u00fchendislik Derg."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khandagale, S.D., and Patil, S.M. (2024). Classification of Defects in Photovoltaic Cells from Electroluminescence Images using Advanced Deep Learning Techniques. Proceedings of the 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI), IEEE.","DOI":"10.1109\/IDICAIEI61867.2024.10842697"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, J., Bi, L., Sun, P., Jiao, X., Ma, X., Lei, X., and Luo, Y. (2022). Deep-learning-based automatic detection of photovoltaic cell defects in electroluminescence images. Sensors, 23.","DOI":"10.3390\/s23010297"},{"key":"ref_18","first-page":"201","article-title":"Introduction to Surface Damage on Solar Panels with Feature Extraction using Statistical Methods","volume":"7","author":"Wiliani","year":"2025","journal-title":"J. Asiimetrik J. Ilm. Rekayasa Inov."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ariffin, Z.K., Shahbudin, S., Zolkapli, M., Rahman, F.Y.A., and Suliman, S.I. (2025). Photovoltaic (PV) Module Defects Classification Analysis Using EfficientNet Architecture in Electroluminescence (EL) Images. Proceedings of the 2025 IEEE 16th Control and System Graduate Research Colloquium (ICSGRC), IEEE.","DOI":"10.1109\/ICSGRC65918.2025.11159738"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/TII.2022.3162846","article-title":"PVEL-AD: A large-scale open-world dataset for photovoltaic cell anomaly detection","volume":"19","author":"Su","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/JPHOTOV.2021.3131059","article-title":"Automated defect detection and localization in photovoltaic cells using semantic segmentation of electroluminescence images","volume":"12","author":"Fioresi","year":"2021","journal-title":"IEEE J. Photovolt."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"497","DOI":"10.55525\/tjst.1445681","article-title":"Automatic classification of defective photovoltaic module cells based on a novel CNN-PCA-SVM deep hybrid model in electroluminescence images","volume":"19","year":"2024","journal-title":"Turk. J. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ayturan, K., Sar\u0131kam\u0131\u015f, B., Ak\u015fahin, M.F., and Kutbay, U. (2025). SPHERE: Benchmarking YOLO vs. CNN on a novel dataset for high-accuracy solar panel defect detection in renewable energy systems. Appl. Sci., 15.","DOI":"10.3390\/app15094880"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rahman, A. (2025). Solar panel surface defect and dust detection: Deep learning approach. J. Imaging, 11.","DOI":"10.3390\/jimaging11090287"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"150","DOI":"10.14445\/23488379\/IJEEE-V11I5P114","article-title":"Electroluminescence images for solar cell fault detection using deep learning for binary and multiclass classification","volume":"11","author":"Almashhadani","year":"2024","journal-title":"SSRG Int. J. Electr. Electron. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13","DOI":"10.21741\/9781644903216-2","article-title":"Electroluminescence image-based defective photovoltaic (solar) cell detection using a modified deep convolutional neural network","volume":"24","author":"Mewada","year":"2024","journal-title":"Renew. Energy Gener. Appl. ICREGA"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1049\/iet-rpg.2019.1342","article-title":"Photovoltaic cell defect classification using convolutional neural network and support vector machine","volume":"14","author":"Ahmad","year":"2020","journal-title":"IET Renew. Power Gener."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"120546","DOI":"10.1016\/j.eswa.2023.120546","article-title":"Photovoltaic cell defect classification based on integration of residual-inception network and spatial pyramid pooling in electroluminescence images","volume":"229","author":"Acikgoz","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_29","first-page":"217","article-title":"Classification of Solar Cells EL Images with Different Busbars Via Deep Learning Models","volume":"7","year":"2024","journal-title":"Sak. Univ. J. Comput. Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rozi, M.W.F.M., and Shahbudin, S. (2023). Photovoltaic module defects classification analysis using ShuffleNet architecture in electroluminescence images. Proceedings of the 2023 9th International Conference on Computer and Communication Engineering (ICCCE), IEEE.","DOI":"10.1109\/ICCCE58854.2023.10246047"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"589","DOI":"10.35234\/fumbd.1099000","article-title":"Elektrol\u00fcminesans G\u00f6r\u00fcnt\u00fclerde Ar\u0131zal\u0131 Fotovoltaik Panel H\u00fccrelerin Evri\u015fimli Sinir A\u011f\u0131 ile Otomatik S\u0131n\u0131fland\u0131r\u0131lmas\u0131","volume":"34","author":"Korkmaz","year":"2022","journal-title":"F\u0131rat \u00dcniversitesi M\u00fchendislik Bilim. Derg."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"124230","DOI":"10.1016\/j.eswa.2024.124230","article-title":"An improved hybrid solar cell defect detection approach using Generative Adversarial Networks and weighted classification","volume":"252","author":"Demirci","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"085404","DOI":"10.1088\/1361-6501\/adf657","article-title":"Defect analysis using electroluminescence imaging in photovoltaic modules","volume":"36","author":"Zhang","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Manandhar, P., Rafiq, H., Kumar, S., Qureshi, O.A., and Rodriguez-Ubinas, E. (2023). Identification of micro-defects in solar cells using electroluminescence images via hybrid approach. Proceedings of the 2023 Middle East and North Africa Solar Conference (MENA-SC), IEEE.","DOI":"10.1109\/MENA-SC54044.2023.10374502"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Alanazi, M., and Alamri, Y.A. (2025). SolarFaultAttentionNet: Dual-Attention Framework for Enhanced Photovoltaic Fault Classification. Inventions, 10.","DOI":"10.3390\/inventions10050091"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Niu, M., Li, J., Li, M., Zhao, Y., Liu, Y., and Wen, S. (2024). Defect Detection in Solar Panels Based on PV-Flow. Proceedings of the 2024 WRC Symposium on Advanced Robotics and Automation (WRC SARA), IEEE.","DOI":"10.1109\/WRCSARA64167.2024.10685672"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Duranay, Z.B. (2023). Fault detection in solar energy systems: A deep learning approach. Electronics, 12.","DOI":"10.3390\/electronics12214397"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"112245","DOI":"10.1016\/j.solener.2023.112245","article-title":"An efficient CNN-based detector for photovoltaic module cells defect detection in electroluminescence images","volume":"267","author":"Liu","year":"2024","journal-title":"Sol. Energy"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"200048","DOI":"10.1016\/j.sasc.2023.200048","article-title":"A benchmark dataset for defect detection and classification in electroluminescence images of PV modules using semantic segmentation","volume":"5","author":"Pratt","year":"2023","journal-title":"Syst. Soft Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sachdeva, B., Gill, K.S., Kumar, M., and Rawat, R.S. (2024). Future-Proofing Solar Panel Maintenance: Early Detection Using VGG16-Based Systems. Proceedings of the 2024 10th International Conference on Electrical Energy Systems (ICEES), IEEE.","DOI":"10.1109\/ICEES61253.2024.10776881"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tella, H., Mohandes, M., Liu, B., Rehman, S., and Al-Shaikhi, A. (2022). Deep learning system for defect classification of solar panel cells. Proceedings of the 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN), IEEE.","DOI":"10.1109\/CICN56167.2022.10008277"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"579","DOI":"10.37391\/ijeer.100328","article-title":"Enhanced Classification of Faults of Photovoltaic Module Through Generative Adversarial Network","volume":"10","author":"Bharathi","year":"2022","journal-title":"Int. J. Electr. Electron. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Boubaker, S., Kamel, S., Ghazouani, N., and Mellit, A. (2023). Assessment of machine and deep learning approaches for fault diagnosis in photovoltaic systems using infrared thermography. Remote Sens., 15.","DOI":"10.3390\/rs15061686"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"67","DOI":"10.7836\/kses.2025.45.4.067","article-title":"A Study on the Optimization of Fine-tuning and Dataset for Defect Detection Models in Photovoltaic Modules Images","volume":"45","author":"Kim","year":"2025","journal-title":"J. Korean Sol. Energy Soc."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Eesaar, H., Joe, S., Rehman, M.U., Jang, Y., and Chong, K.T. (2023). SEiPV-Net: An efficient deep learning framework for autonomous multi-defect segmentation in electroluminescence images of solar photovoltaic modules. Energies, 16.","DOI":"10.3390\/en16237726"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100792","DOI":"10.1109\/ACCESS.2024.3431098","article-title":"Attention-based SegNet: Toward refined semantic segmentation of PV modules defects","volume":"12","author":"Mazen","year":"2024","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"17295","DOI":"10.1364\/OE.517341","article-title":"Deep learning-based method for defect detection in electroluminescent images of polycrystalline silicon solar cells","volume":"32","author":"Liu","year":"2024","journal-title":"Opt. Express"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"111790","DOI":"10.1016\/j.engappai.2025.111790","article-title":"Advancing automatic photovoltaic defect detection using semi-supervised semantic segmentation of electroluminescence images","volume":"160","author":"Jha","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1002\/pip.3395","article-title":"Deep learning-based automatic detection of multitype defects in photovoltaic modules and application in real production line","volume":"29","author":"Zhao","year":"2021","journal-title":"Prog. Photovolt. Res. Appl."},{"key":"ref_50","first-page":"446","article-title":"Investigation of crack detection in solar cells by context integration and attention enhanced feature pyramid network","volume":"Volume 13156","author":"Chen","year":"2024","journal-title":"Proceedings of the Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xu, W., Shi, Y., Yang, R., Ye, B., and Qiang, H. (2024). Automatic classification of defective solar panels in electroluminescence images based on random connection network. Electronics, 13.","DOI":"10.3390\/electronics13132429"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1147\/rd.266.0647","article-title":"Document analysis system","volume":"26","author":"Wong","year":"1982","journal-title":"IBM J. Res. Dev."},{"key":"ref_53","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_54","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., and Liu, W. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_56","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_57","unstructured":"Iandola, F.N. (2016). SqueezeNet: AlexNet-level accuracy with 50\u00d7 fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 28\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_61","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_62","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_64","unstructured":"Tan, M., and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201322). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2450\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T10:41:24Z","timestamp":1776854484000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,16]]},"references-count":65,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26082450"],"URL":"https:\/\/doi.org\/10.3390\/s26082450","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,16]]}}}