{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T14:27:40Z","timestamp":1772461660301,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional power equipment defect-detection relies on manual verification, which places a high demand on the verifier\u2019s experience, as well as a high workload and low efficiency, which can lead to false detection and missed detection. The Mask of the regions with CNN features (Mask RCNN) deep learning model is used to provide a defect-detection approach based on the Mask RCNN of Attention, Rotation, Genetic algorithm (ARG-Mask RCNN), which employs infrared imaging as the data source to assess the features of damaged insulators. For the backbone network of Mask RCNN, the structure of Residual Network 101 (ResNet101) is improved and the attention mechanism is added, which makes the model more alert to small targets and can quickly identify the location of small targets, improve the loss function, integrate the rotation mechanism into the loss function formula, and generate an anchor frame where a rotation angle is used to accurately locate the fault location. The initial hyperparameters of the network are improved, and the Genetic Algorithm Combined with Gradient Descent (GA-GD) algorithm is used to optimize the model hyperparameters, so that the model training results are as close to the global best as possible. The experimental results show that the average accuracy of the insulator fault-detection method proposed in this paper is as high as 98%, and the number of frames per second (FPS) is 5.75, which provides a guarantee of the safe, stable, and reliable operation of our country\u2019s power system.<\/jats:p>","DOI":"10.3390\/s22134720","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T23:11:19Z","timestamp":1655939479000},"page":"4720","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3683-7641","authenticated-orcid":false,"given":"Ming","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116039, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1381-9667","authenticated-orcid":false,"given":"Jue","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116039, China"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116039, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2752","DOI":"10.2193\/2005-752","article-title":"Effectiveness of avian predator perch deterrents on electric transmission lines","volume":"71","author":"Lammers","year":"2007","journal-title":"J. Wildl. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"59022","DOI":"10.1109\/ACCESS.2019.2914766","article-title":"A recognition technology of transmission lines conductor break and surface damage based on aerial image","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1049\/hve.2019.0091","article-title":"Research on automatic location and recognition of insulators in substation based on YOLOv3","volume":"5","author":"Liu","year":"2020","journal-title":"High Volt."},{"key":"ref_4","first-page":"75","article-title":"Monitoring of leakage current for composite insulators and electrical devices","volume":"21","author":"Amin","year":"2009","journal-title":"Rev. Adv. Mater. Sci"},{"key":"ref_5","unstructured":"McDermid, W., Grant, D., Glodjo, A., and Bromley, J. (2001, January 18). Analysis of converter transformer failures and application of periodic on-line partial discharge measurements. Proceedings of the Electrical Insulation Conference and Electrical Manufacturing and Coil Winding Conference (Cat. No. 01CH37264), Cincinnati, OH, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TDEI.2015.005225","article-title":"Ultrasonic phased array detection of internal defects in composite insulators","volume":"23","author":"Yuan","year":"2016","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1109\/TDEI.2015.004741","article-title":"Localization of multiple insulators by orientation angle detection and binary shape prior knowledge","volume":"22","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Altaf, M., Akram, T., Khan, M.A., Iqbal, M., Ch, M.M.I., and Hsu, C.-H. (2022). A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals. Sensors, 22.","DOI":"10.3390\/s22052012"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/MIE.2013.2287651","article-title":"Trends in fault diagnosis for electrical machines: A review of diagnostic techniques","volume":"8","author":"Henao","year":"2014","journal-title":"IEEE Ind. Electron. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1109\/TDEI.2017.006665","article-title":"Ultrasonic guided wave-based detection of composite insulator debonding","volume":"24","author":"Deng","year":"2017","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_11","unstructured":"Ji, Y., Tao, X., Jianjun, T., Lan, X., and Zhan-long, Z. (2007, January 14\u201317). Online detection system for contaminated insulators based on ultra-violet pulse method. Proceedings of the 2007 Annual Report-Conference on Electrical Insulation and Dielectric Phenomena, Vancouver, BC, Canada."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2126","DOI":"10.1109\/TDEI.2016.7556487","article-title":"Research of nondestructive methods to test defects hidden within composite insulators based on THz time-domain spectroscopy technology","volume":"23","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103910","DOI":"10.1016\/j.imavis.2020.103910","article-title":"Recent advances in small object detection based on deep learning: A review","volume":"97","author":"Tong","year":"2020","journal-title":"Image Vis. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2289","DOI":"10.1109\/TPWRD.2007.899535","article-title":"Selection of line insulators with respect to ice and Snow\u2014Part I: Context and stresses","volume":"22","author":"Farzaneh","year":"2007","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201323). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_16","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 European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_19","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, Y., Shuang, F., Gao, F., Zhou, X., and Chen, X. (2020). Issd: Improved ssd for insulator and spacer online detection based on uav system. Sensors, 20.","DOI":"10.3390\/s20236961"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q.V. (2019, January 15\u201320). Mnasnet: Platform-aware neural architecture search for mobile. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00293"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wen, Q., Luo, Z., Chen, R., Yang, Y., and Li, G. (2021). Deep learning approaches on defect detection in high resolution aerial images of insulators. Sensors, 21.","DOI":"10.3390\/s21041033"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1016\/j.egyr.2020.09.002","article-title":"High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines","volume":"6","author":"Liu","year":"2020","journal-title":"Energy Rep."},{"key":"ref_24","unstructured":"Arthur, D., and Vassilvitskii, S. (2006). k-Means++: The Advantages of Careful Seeding, Stanford University."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"5345","DOI":"10.1109\/TIM.2020.2965635","article-title":"Automatic fault diagnosis of infrared insulator images based on image instance segmentation and temperature analysis","volume":"69","author":"Wang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., He, K., and Doll\u00e1r, P. (2019, January 15\u201320). Panoptic feature pyramid networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00656"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sastry, K., Goldberg, D., and Kendall, G. (2005). Genetic algorithms. Search Methodologies, Springer.","DOI":"10.1007\/0-387-28356-0_4"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2012). Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"ref_32","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.procs.2015.03.149","article-title":"Face recognition using Gabor filter based feature extraction with anisotropic diffusion as a pre-processing technique","volume":"45","author":"Abhishree","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1109\/COMST.2018.2846401","article-title":"Deep learning for intelligent wireless networks: A comprehensive survey","volume":"20","author":"Mao","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_35","unstructured":"Herdade, S., Kappeler, A., Boakye, K., and Soares, J. (2019). Image captioning: Transforming objects into words. Adv. Neural Inf. Processing Syst., 32."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Issac, J., W\u00fcthrich, M., Cifuentes, C.G., Bohg, J., Trimpe, S., and Schaal, S. (2016, January 6\u201321). Depth-based object tracking using a robust gaussian filter. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487184"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xuan, L., and Hong, Z. (2017, January 24\u201326). An improved canny edge detection algorithm. Proceedings of the 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.","DOI":"10.1109\/ICSESS.2017.8342913"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Huang, S., Li, Y., Li, H., and Hao, H. (2022). Image Detection of Insulator Defects Based on Morphological Processing and Deep Learning. Energies, 15.","DOI":"10.3390\/en15072465"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhou, T., Tulsiani, S., Sun, W., Malik, J., and Efros, A.A. (2016). View synthesis by appearance flow. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46493-0_18"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.jbi.2005.02.008","article-title":"The use of receiver operating characteristic curves in biomedical informatics","volume":"38","author":"Lasko","year":"2005","journal-title":"J. Biomed. Inform."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Al-Bashiri, H., Abdulgabber, M.A., Romli, A., and Kahtan, H. (2018). An improved memory-based collaborative filtering method based on the TOPSIS technique. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0204434"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1055\/s-2005-921034","article-title":"PillCam ESO in esophageal studies: Improved diagnostic yield of 14 frames per second (fps) compared with 4 fps","volume":"38","author":"Koslowsky","year":"2006","journal-title":"Endoscopy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1109\/TCST.2010.2071415","article-title":"Generalized reconstruction-based contributions for output-relevant fault diagnosis with application to the Tennessee Eastman process","volume":"19","author":"Li","year":"2010","journal-title":"IEEE Trans. Control. Syst. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jmsy.2019.03.002","article-title":"Machine vision intelligence for product defect inspection based on deep learning and Hough transform","volume":"51","author":"Wang","year":"2019","journal-title":"J. Manuf. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lin, A., Liu, Y., and Zhang, L. (2021, January 12\u201314). Mushroom Detection and Positioning Method Based on Neural Network. Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC50856.2021.9390669"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mao, H., Netravali, R., and Alizadeh, M. (2017, January 21\u201325). Neural adaptive video streaming with pensieve. Proceedings of the Conference of the ACM Special Interest Group on Data Communication, Los Angeles, CA, USA.","DOI":"10.1145\/3098822.3098843"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TPWRS.2005.861981","article-title":"A classification approach for power distribution systems fault cause identification","volume":"21","author":"Xu","year":"2006","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"015401","DOI":"10.1088\/1361-6501\/aaed0a","article-title":"A robust and high-precision automatic reading algorithm of pointer meters based on machine vision","volume":"30","author":"Ma","year":"2018","journal-title":"Meas. Sci. Technol."},{"key":"ref_50","first-page":"474","article-title":"An accurate and real-time method of self-blast glass insulator location based on faster R-CNN and U-net with aerial images","volume":"5","author":"Ling","year":"2019","journal-title":"CSEE J. Power Energy Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Guo, C., Ren, M., Xia, C., Dong, M., and Wang, B. (2020, January 25\u201327). Fault diagnosis of power equipment based on infrared image analysis. Proceedings of the 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), Dalian, China.","DOI":"10.1109\/AEECA49918.2020.9213457"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4720\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:37:52Z","timestamp":1760139472000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,22]]},"references-count":51,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134720"],"URL":"https:\/\/doi.org\/10.3390\/s22134720","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,22]]}}}