{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T13:03:43Z","timestamp":1780319023868,"version":"3.54.1"},"reference-count":41,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:00:00Z","timestamp":1656374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of State Grid Hebei Information and tTelecommunication Branch","award":["SGHEXT00SJJS2100036"],"award-info":[{"award-number":["SGHEXT00SJJS2100036"]}]},{"name":"Science and Technology Project of State Grid Hebei Information and tTelecommunication Branch","award":["U21A20486"],"award-info":[{"award-number":["U21A20486"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China Joint Fund Project","doi-asserted-by":"publisher","award":["SGHEXT00SJJS2100036"],"award-info":[{"award-number":["SGHEXT00SJJS2100036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China Joint Fund Project","doi-asserted-by":"publisher","award":["U21A20486"],"award-info":[{"award-number":["U21A20486"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The detection of insulator umbrella disc shedding is very important to the stable operation of a transmission line. In order to accomplish the accurate detection of the insulator umbrella disc shedding in foggy weather, a two-stage detection model combined with a defogging algorithm is proposed. In the dehazing stage of insulator images, solving the problem of real hazy image data is difficult; the foggy images are dehazed by the method of synthetic foggy images training and real foggy images fine-tuning. In the detection stage of umbrella disc shedding, a small object detection algorithm named FA-SSD is proposed to solve the problem of the umbrella disc shedding occupying only a small proportion of an aerial image. On the one hand, the shallow feature information and deep feature information are fused to improve the feature extraction ability of small targets; on the other hand, the attention mechanism is introduced to strengthen the feature extraction network\u2019s attention to the details of small targets and improve the model\u2019s ability to detect the umbrella disc shedding. The experimental results show that our model can accurately detect the insulator umbrella disc shedding defect in the foggy image; the accuracy of the defect detection is 0.925, and the recall is 0.841. Compared with the original model, it improved by 5.9% and 8.6%, respectively.<\/jats:p>","DOI":"10.3390\/s22134871","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T01:48:38Z","timestamp":1656467318000},"page":"4871","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Insulator Umbrella Disc Shedding Detection in Foggy Weather"],"prefix":"10.3390","volume":"22","author":[{"given":"Rui","family":"Xin","sequence":"first","affiliation":[{"name":"State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5501-7115","authenticated-orcid":false,"given":"Junying","family":"Wu","sequence":"additional","affiliation":[{"name":"State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7109-9173","authenticated-orcid":false,"given":"Ke","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Automation, North China Electric Power University, Baoding 071003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5677-197X","authenticated-orcid":false,"given":"Xinying","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Automation, North China Electric Power University, Baoding 071003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2997-5840","authenticated-orcid":false,"given":"Yongjie","family":"Zhai","sequence":"additional","affiliation":[{"name":"Department of Automation, North China Electric Power University, Baoding 071003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2808","DOI":"10.1109\/TIM.2018.2867966","article-title":"Uncertainty bounds of transmission line parameters estimated from synchronized measurements","volume":"68","author":"Asprou","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6080","DOI":"10.1109\/TIM.2020.2969057","article-title":"Detection method based on automatic visual shape clustering for pin-missing defect in transmission lines","volume":"69","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9699","DOI":"10.1109\/TIE.2017.2716862","article-title":"Acoustic fault detection technique for high-power insulators","volume":"64","author":"Park","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Xia, H., Yang, B., Li, Y., and Wang, B. (2022). An Improved CenterNet Model for Insulator Defect Detection Using Aerial Imagery. Sensors, 22.","DOI":"10.3390\/s22082850"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"687","DOI":"10.12720\/jcm.9.9.687-692","article-title":"Unmanned Aerial Vehicles for Power Line Inspection: A Cooperative Way in Platforms and Communications","volume":"9","author":"Deng","year":"2014","journal-title":"J. Commun."},{"key":"ref_8","first-page":"1","article-title":"Hybrid knowledge r-cnn for transmission line multifitting detection","volume":"70","author":"Zhai","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/JETCAS.2020.3000103","article-title":"Artificial intelligence for 5G and beyond 5G: Implementations, algorithms, and optimizations","volume":"10","author":"Zhang","year":"2020","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4122","DOI":"10.1109\/TII.2021.3115697","article-title":"Industrial IoT in 5G-and-beyond networks: Vision, architecture, and design trends","volume":"18","author":"Mahmood","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1109\/83.841534","article-title":"Adaptive image contrast enhancement using generalizations of histogram equalization","volume":"9","author":"Stark","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cviu.2017.08.002","article-title":"Efficient single image dehazing and denoising: An efficient multi-scale correlated wavelet approach","volume":"162","author":"Liu","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"155732","DOI":"10.1109\/ACCESS.2020.3019354","article-title":"Color correction based on cfa and enhancement based on retinex with dense pixels for underwater images","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","first-page":"2341","article-title":"Single image haze removal using dark channel prior","volume":"33","author":"He","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, F., Meng, X., Feng, Y., and Su, Z. (2022). SNPD: Semi-Supervised Neural Process Dehazing Network with Asymmetry Pseudo Labels. Symmetry, 14.","DOI":"10.3390\/sym14040806"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103357","DOI":"10.1016\/j.cviu.2022.103357","article-title":"Robust detection of dehazed image via dual-stream CNNs with adaptive feature fusion","volume":"217","author":"Chen","year":"2022","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, W., Zhao, Y., Feng, L., and Tang, J. (2021). Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze. Electronics, 10.","DOI":"10.3390\/electronics10222868"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gao, G., Cao, J., Bao, C., Hao, Q., Ma, A., and Li, G. (2022). A Novel Transformer-Based Attention Network for Image Dehazing. Sensors, 22.","DOI":"10.3390\/s22093428"},{"key":"ref_21","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_22","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_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3106112","article-title":"Insulator Surface Breakage Recognition Based on Multiscale Residual Neural Network","volume":"70","author":"She","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3127641","article-title":"InsuDet: A Fault Detection Method for Insulators of Overhead Transmission Lines Using Convolutional Neural Networks","volume":"70","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_26","first-page":"1","article-title":"An Insulator in Transmission Lines Recognition and Fault Detection Model Based on Improved Faster RCNN","volume":"70","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst., 28, Available online: https:\/\/proceedings.neurips.cc\/paper\/2015\/hash\/14bfa6bb14875e45bba028a21ed38046-Abstract.html."},{"key":"ref_28","first-page":"2117","article-title":"Feature pyramid networks for object detection","volume":"36","author":"Lin","year":"2017","journal-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, W., Ren, G., Yu, R., Guo, S., Zhu, J., and Zhang, L. (2021). Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions. arXiv.","DOI":"10.1609\/aaai.v36i2.20072"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2505","DOI":"10.1109\/TTE.2021.3080690","article-title":"A Feature Fusion Method to Improve the Driving Obstacle Detection Under Foggy Weather","volume":"7","author":"He","year":"2021","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4230","DOI":"10.1109\/TITS.2020.3014013","article-title":"Vehicle detection and tracking in adverse weather using a deep learning framework","volume":"22","author":"Hassaballah","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, 8\u201316 August 2016.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wang, Y., Yang, Y., and Liu, D. PSD: Principled synthetic-to-real dehazing guided by physical priors. Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20\u201325 June 2021.","DOI":"10.1109\/CVPR46437.2021.00710"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","article-title":"Benchmarking single-image dehazing and beyond","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","unstructured":"Li, Z., and Zhou, F. (2017). FSSD: Feature fusion single shot multibox detector. arXiv."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","unstructured":"Li, Y., Chang, Y., Gao, Y., Yu, C., and Yan, L. (2022, January 21\u201324). Physically Disentangled Intra-and Inter-Domain Adaptation for Varicolored Haze Removal. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00575"},{"key":"ref_40","unstructured":"Yuntong, Y., Changfeng, Y., Yi, C., Lin, Z., Xile, Z., Luxin, Y., and Yonghong, T. (2022). Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity. arXiv."},{"key":"ref_41","unstructured":"Li, Y., Chang, Y., Yu, C., and Yan, L. (2022, May 17). Close the Loop: A Unified Bottom-Up and Top-Down Paradigm for Joint Image Deraining and Segmentation. Available online: https:\/\/www.aaai.org\/AAAI22Papers\/AAAI-678.LiY.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4871\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:39:52Z","timestamp":1760139592000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":41,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134871"],"URL":"https:\/\/doi.org\/10.3390\/s22134871","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,28]]}}}