{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:35:47Z","timestamp":1784392547612,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["JCYJ20190808142613246"],"award-info":[{"award-number":["JCYJ20190808142613246"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China Society of Automotive Engineers","award":["Young Elite Scientists Sponsorship Program"],"award-info":[{"award-number":["Young Elite Scientists Sponsorship Program"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.<\/jats:p>","DOI":"10.3390\/s21041033","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T20:31:51Z","timestamp":1612384311000},"page":"1033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2774-2605","authenticated-orcid":false,"given":"Qiaodi","family":"Wen","sequence":"first","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Luo","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruitao","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7889-4695","authenticated-orcid":false,"given":"Guofa","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2503","DOI":"10.1109\/TDEI.2014.004485","article-title":"Failure analysis of decay-like fracture of composite insulator","volume":"21","author":"Wang","year":"2014","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1631\/jzus.A1900341","article-title":"Catenary insulator defect detection based on contour features and gray similarity matching","volume":"21","author":"Tan","year":"2020","journal-title":"J. Zhejiang Univ. Sci. A"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"022056","DOI":"10.1088\/1742-6596\/1187\/2\/022056","article-title":"Transmission line insulator fault detection based on ultrasonic technology","volume":"1187","author":"Yao","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gao, Y., Xu, A., Ouyang, Q., Zhang, L., and Wang, Y. (2015, January 27\u201328). Insulator Defect Detection Technology Research. Proceedings of the 3rd International Conference on Material Mechanical and Manufacturing Engineering (IC3ME 2015), Guangzhou, China.","DOI":"10.2991\/ic3me-15.2015.241"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1109\/JSTARS.2012.2197672","article-title":"A texture segmentation algorithm based on pca and global minimization active contour model for aerial insulator images","volume":"5","author":"Wu","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","unstructured":"Zhang, J., and Yang, R. (2006, January 20\u201324). Insulators Recognition for 220kv\/330kv High-voltage Live-line Cleaning Robot. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, W., Ye, G., Huang, F., Wang, S., and Chang, W. (2010, January 16\u201318). Recognition of Insulator Based on Developed MPEG-7 Texture Feature. Proceedings of the 2010 3rd International Congress on Image and Signal Processing, Yantai, China.","DOI":"10.1109\/CISP.2010.5648283"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/LGRS.2014.2369525","article-title":"A robust insulator detection algorithm based on local features and spatial orders for aerial images","volume":"12","author":"Liao","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.4028\/www.scientific.net\/AMR.936.2184","article-title":"Transmission line insulators detection based on KAZE algorithm","volume":"936","author":"Li","year":"2014","journal-title":"AMR"},{"key":"ref_10","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, San Diego, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, S., Bauckhage, C., and Cremers, A.B. (2014, January 23\u201328). Informed Haar-like Features Improve Pedestrian Detection. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.126"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JEI.26.6.063014","article-title":"Automatic identification and location technology of glass insulator self-shattering","volume":"26","author":"Huang","year":"2017","journal-title":"J. Electron. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Zhen, Z., Zhang, L., Qi, Y., Kong, Y., and Zhang, K. (2019). Insulator detection method in inspection image based on improved faster R-CNN. Energies, 12.","DOI":"10.3390\/en12071204"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"012147","DOI":"10.1088\/1742-6596\/1069\/1\/012147","article-title":"Cracked insulator detection based on R-FCN","volume":"1069","author":"Li","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201320). You Only Look Once: Unified, Real-time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","first-page":"8","article-title":"SSD: Single shot multibox detector","volume":"12","author":"Liu","year":"2015","journal-title":"Eur. Conf. Comput. Vision"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., and Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv.","DOI":"10.3115\/v1\/P14-1062"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liao, G.-P., Yang, G.-J., Tong, W.-T., Gao, W., Lv, F.-L., and Gao, D. (2019, January 10\u201320). Study on Power Line Insulator Defect Detection via Improved Faster Region-based Convolutional Neural Network. Proceedings of the 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China.","DOI":"10.1109\/ICCSNT47585.2019.8962497"},{"key":"ref_21","first-page":"606","article-title":"Deep learning based object distance measurement method for binocular stereo vision blind area","volume":"9","author":"Zhang","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rcim.2019.03.001","article-title":"Real-time detection of surface deformation and strain in recycled aggregate concrete-filled steel tubular columns via four-ocular vision","volume":"59","author":"Tang","year":"2019","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tang, Y., Chen, M., Lin, Y., Huang, X., Huang, K., He, Y., and Li, L. (2020). Vision-based three-dimensional reconstruction and moni-toring of large-scale steel tubular structures. Adv. Civ. Eng., 1236021.","DOI":"10.1155\/2020\/1236021"},{"key":"ref_24","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 CVPR, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_27","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_28","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. arXiv.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 CVPR, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_33","unstructured":"GitHub Repository (2021, February 01). Deep learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators, Available online: https:\/\/github.com\/TaoTao9\/Deep-learning-approaches-on-defect-detection-in-high-resolu-tion-aerial-images-of-insulators."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Qassim, H., Verma, A., and Feinzimer, D. (2018, January 8\u201310). Compressed Residual-VGG16 CNN Model for Big Data Places Image Recognition. Proceedings of the 2018 CCWC, Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2018.8301729"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 CVPR, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"211164","DOI":"10.1109\/ACCESS.2020.3036620","article-title":"Detection of road objects with mall appearance in images for autonomous driving in various traffic situations using a deep learning based approach","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2018). Mask R-CNN. arXiv.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_38","unstructured":"Arthur, D., and Vassilvitskii, S. (2007, January 7\u20139). K-Means++: The Advantages of Careful Seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, New Orleans, LA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"012062","DOI":"10.1088\/1757-899X\/790\/1\/012062","article-title":"Object detection algorithm for improving non-maximum suppression using GIoU","volume":"790","author":"Hou","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"Hu, Y., Wen, G., Luo, M., Dai, D., Ma, J., and Yu, Z. (2018). competitive inner-imaging squeeze and excitation for residual network. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2018). Focal loss for dense object detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s41074-018-0047-6","article-title":"An multi-scale learning network with depthwise separable convolutions","volume":"10","author":"Wang","year":"2018","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_44","unstructured":"Goyal, P., Doll\u00e1r, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K. (2018). Accurate, large min-ibatch SGD: Training imagenet in 1 hour. arXiv."},{"key":"ref_45","unstructured":"Hensman, P., and Masko, D. (2015). The Impact of Imbalanced Training Data for Convolutional Neural Networks. [Ph.D. Thesis, KTH Royal Institute of Technology]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8889","DOI":"10.1109\/TIE.2019.2945295","article-title":"Deep learning approaches on pedestrian detection in hazy weather","volume":"67","author":"Li","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_47","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_48","first-page":"012062","article-title":"RepPoints: Point set representation for object detection","volume":"790","author":"Yang","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"102820","DOI":"10.1016\/j.trc.2020.102820","article-title":"Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios","volume":"122","author":"Li","year":"2021","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity mappings in deep residual networks. arXiv.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106617","DOI":"10.1016\/j.knosys.2020.106617","article-title":"A deep learning based image enhancement approach for autonomous driving at night","volume":"213","author":"Li","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1007\/s11263-019-01204-1","article-title":"CornerNet: Detecting objects as paired keypoints","volume":"128","author":"Law","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhuo, J., and Krahenbuhl, P. (2019, January 15\u201321). Bottom-up Object Detection by Grouping Extreme and Center Points. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00094"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1033\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:19:25Z","timestamp":1760159965000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1033"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,3]]},"references-count":53,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041033"],"URL":"https:\/\/doi.org\/10.3390\/s21041033","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,3]]}}}