{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:36:45Z","timestamp":1775284605477,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T00:00:00Z","timestamp":1703808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Projects of Henan Province","award":["231111220500"],"award-info":[{"award-number":["231111220500"]}]},{"name":"Key Research and Development Projects of Henan Province","award":["232300420091"],"award-info":[{"award-number":["232300420091"]}]},{"name":"Key Research and Development Projects of Henan Province","award":["222300420093"],"award-info":[{"award-number":["222300420093"]}]},{"name":"Natural Science Foundation of Henan","award":["231111220500"],"award-info":[{"award-number":["231111220500"]}]},{"name":"Natural Science Foundation of Henan","award":["232300420091"],"award-info":[{"award-number":["232300420091"]}]},{"name":"Natural Science Foundation of Henan","award":["222300420093"],"award-info":[{"award-number":["222300420093"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the challenges of balancing accuracy and speed, as well as the parameters and FLOPs in current insulator defect detection, we propose an enhanced insulator defect detection algorithm, ML-YOLOv5, based on the YOLOv5 network. The backbone module incorporates depthwise separable convolution, and the feature fusion C3 module is replaced with the improved C2f_DG module. Furthermore, we enhance the feature pyramid network (MFPN) and employ knowledge distillation using YOLOv5m as the teacher model. Experimental results demonstrate that this approach achieved a 46.9% reduction in parameter count and a 43.0% reduction in FLOPs, while maintaining an FPS of 63.6. It exhibited good accuracy and detection speed on both the CPLID and IDID datasets, making it suitable for real-time inspection of high-altitude insulator defects.<\/jats:p>","DOI":"10.3390\/s24010204","type":"journal-article","created":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T03:28:41Z","timestamp":1703820521000},"page":"204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Insulator Defect Detection Based on ML-YOLOv5 Algorithm"],"prefix":"10.3390","volume":"24","author":[{"given":"Tong","family":"Wang","sequence":"first","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China"},{"name":"College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6594-5531","authenticated-orcid":false,"given":"Yidi","family":"Zhai","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China"},{"name":"College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhang","family":"Li","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China"},{"name":"College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0404-8167","authenticated-orcid":false,"given":"Weihua","family":"Wang","sequence":"additional","affiliation":[{"name":"China Special Equipment Inspection and Research Institute, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoyong","family":"Ye","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China"},{"name":"College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaobo","family":"Jin","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China"},{"name":"College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"El-Hag, A., Mukhopadhyay, S., Al-Ali, K., and Al-Saleh, A. (2017, January 16\u201318). An intelligent system for acoustic inspection of outdoor insulators. Proceedings of the 2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), Rupnagar, India.","DOI":"10.1109\/CATCON.2017.8280197"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12051","DOI":"10.1007\/s11042-016-3981-2","article-title":"Fault detection of insulator based on saliency and adaptive morphology","volume":"76","author":"Zhai","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Varghese, A., Gubbi, J., Sharma, H., and Balamuralidhar, P. (2017, January 14\u201319). Power infrastructure monitoring and damage detection using drone captured images. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966053"},{"key":"ref_4","first-page":"3827","article-title":"Power defect identification method based on fixed-point adaptive selection convolution neural network","volume":"47","author":"Dai","year":"2021","journal-title":"High Volt. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1049\/gtd2.12180","article-title":"Fault detection and classification of an HVDC transmission line using a heterogeneous machine learning algorithm","volume":"20","author":"Ghashghaei","year":"2021","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhou, M., Wang, J., and Li, B. (2022). ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN. Sensors, 22.","DOI":"10.3390\/s22134720"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"362","DOI":"10.4028\/www.scientific.net\/AMM.433-435.362","article-title":"Insulator Recognition Based on Moments Invariant Features and Cascade AdaBoost Classifier","volume":"433","author":"He","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"ref_8","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_9","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_10","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_12","unstructured":"Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (2017). DSSD: Deconvolutional single shot detector. arXiv."},{"key":"ref_13","unstructured":"Li, Z.-X., and Zhou, F.-Q. (2017). FSSD: Feature fusion single shot multibox detector. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, C., Wu, Y., Liu, J., and Han, J. (2021). MTI-YOLO: A light-weight and real-time deep neural network for insulator detection in complex aerial images. Energies, 14.","DOI":"10.3390\/en14051426"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1109\/TPWRD.2019.2944741","article-title":"IN-YOLO: Real-Time Detection of Outdoor High Voltage Insulators Using UAV Imaging","volume":"35","author":"Sadykova","year":"2020","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Adou, M.W., Xu, H., and Chen, G. (2019, January 25\u201327). Insulator Faults Detection Based on Deep Learning. Proceedings of the 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), Xiamen, China.","DOI":"10.1109\/ICASID.2019.8925094"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.egyr.2022.08.027","article-title":"Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model","volume":"8","author":"Li","year":"2022","journal-title":"Energy Rep."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Feng, Z., Guo, L., Huang, D., and Li, R. (2021, January 14\u201316). Electrical Insulator Defects Detection Method Based on YOLOv5. Proceedings of the 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS), Suzhou, China.","DOI":"10.1109\/DDCLS52934.2021.9455519"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Han, J.M., and Zhong, Y. (2019). A method of insulator faults detection in aerial images for High-Voltage transmission lines inspection. Appl. Sci., 9.","DOI":"10.3390\/app9102009"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, Y., and Chen, W. (2022). Detection of Glass Insulators Using Deep Neural Networks Based on Optical Imaging. Remote Sens., 14.","DOI":"10.3390\/rs14205153"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, W., Li, Y., and Zhao, Z. (2022). Missing-Sheds Granularity Estimation of Glass Insulators Using Deep Neural Networks Based on Optical Imaging. Sensors, 22.","DOI":"10.3390\/s22051737"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, C., Wu, Y., Liu, J., Sun, Z., and Xu, H. (2021). Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model. Appl. Sci., 11.","DOI":"10.3390\/app11104647"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Han, J., Yang, Z., Xu, H., Hu, G., Zhang, C., Li, H., Lai, S., and Zeng, H. (2020). Search Like an Eagle: A Cascaded Model for Insulator Missing Faults Detection in Aerial Images. Energies, 13.","DOI":"10.3390\/en13030713"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108982","DOI":"10.1016\/j.ijepes.2023.108982","article-title":"Hybrid-YOLO for classification of insulators defects in transmission lines based on UAV","volume":"148","author":"Souza","year":"2023","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, S., Deng, J., Huang, Y., Ling, L., and Han, T. (2022). Research on Insulator Defect Detection Based on an Improved MobilenetV1-YOLOv4. Entropy, 24.","DOI":"10.3390\/e24111588"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, J., Liu, C., Wu, Y., Xu, H., and Sun, Z. (2021). An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images. Energies, 14.","DOI":"10.3390\/en14144365"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"012007","DOI":"10.1088\/1742-6596\/2181\/1\/012007","article-title":"Insulator defect detection algorithm based on a lightweight network","volume":"2181","author":"Lan","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wei, X., Zhang, L., Yu, L., Chen, Y., and Tu, M. (2023). YOLO v7-ECA-PConv-NWD Detects Defective Insulators on Transmission Lines. Electronics, 12.","DOI":"10.3390\/electronics12183969"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Guo, J., Li, T., and Du, B. (2023). Segmentation Head Networks with Harnessing Self-Attention and Transformer for Insulator Surface Defect Detection. Appl. Sci., 13.","DOI":"10.3390\/app13169109"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, H., Chen, J., Hu, J., and Zheng, E. (2023). Insu-YOLO: An Insulator Defect Detection Algorithm Based on Multiscale Feature Fusion. Electronics, 12.","DOI":"10.3390\/electronics12153210"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shuang, F., Han, S., Li, Y., and Lu, T. (2023). RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark. Drones, 7.","DOI":"10.3390\/drones7020125"},{"key":"ref_33","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mehta, R., and Ozturk, C. (2018, January 8\u201314). Object Detection at 200 Frames Per Second. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany. Part V 15.","DOI":"10.1007\/978-3-030-11021-5_41"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1109\/TSMC.2018.2871750","article-title":"Detection of Power Line Insulator Defects Using Aerial Images Analyzed with Convolutional Neural Networks","volume":"50","author":"Tao","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_36","unstructured":"Lewis, D., and Kulkarni, P. (2022, September 20). Insulator Defect Detection. Available online: https:\/\/ieee-dataport.org\/competitions\/insulator-defect-detection."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, Y., Xin, M., Liao, J., and Xie, Q. (2023). A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5. Sensors, 23.","DOI":"10.20944\/preprints202305.0796.v1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bao, W., Du, X., Wang, N., Yuan, M., and Yang, X. (2022). A Defect Detection Method Based on BC-YOLO for Transmission Line Components in UAV Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14205176"},{"key":"ref_39","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 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102635","DOI":"10.1109\/ACCESS.2023.3316266","article-title":"Research on Defect Detection Method of Railway Transmission Line Insulators Based on GC-YOLO","volume":"11","author":"Ding","year":"2023","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"043014","DOI":"10.1117\/1.JEI.32.4.043014","article-title":"MI-YOLO: More information based YOLO for insulator defect detection","volume":"32","author":"Luan","year":"2023","journal-title":"J. Electron. Imaging"},{"key":"ref_42","first-page":"7113765","article-title":"Insulators\u2019 Identification and Missing Defect Detection in Aerial Images Based on Cascaded YOLO Models","volume":"2022","author":"Liu","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, X.B., Zhang, Y., Hu, M., and Ju, X. (November, January 30). Insulator defect detection based on YOLO and SPP-Net. Proceedings of the 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Bangkok, Thailand.","DOI":"10.1109\/ICBASE51474.2020.00092"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"269","DOI":"10.3233\/AIC-220318","article-title":"Fully Automated Neural Network Framework for Pulmonary Nodules Detection and Segmentation","volume":"36","author":"Xiong","year":"2023","journal-title":"AI Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/204\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:44:02Z","timestamp":1760132642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,29]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010204"],"URL":"https:\/\/doi.org\/10.3390\/s24010204","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,29]]}}}