{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:04:43Z","timestamp":1764842683864,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Grid Hebei Electric Power Provincial Company Science and Technology Project Fund Grant Project","award":["kj2021-016"],"award-info":[{"award-number":["kj2021-016"]}]},{"name":"Electric Power Research Institute of State Grid Hebei Electric Power Co., Ltd.","award":["kj2021-016"],"award-info":[{"award-number":["kj2021-016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The accurate detection of insulators is an important prerequisite for insulator fault diagnosis. To solve the problem of background interference and overlap caused by the axis-aligned bounding boxes in the tilting insulator detection tasks, we construct an improved detection architecture according to the scale and tilt features of the insulators from several perspectives, such as bounding box representation, loss function, and anchor box construction. A new orientation detection method for tilting insulators based on angle regression and priori constraints is put forward in this paper. Ablation tests and comparative validation tests were conducted on a self-built aerial insulator image dataset. The results show that the detection accuracy of our model was increased by 7.98% compared with that of the baseline, and the overall detection accuracy reached 82.33%. Moreover, the detection effect of our method was better than that of the YOLOv5 detection model and other orientation detection models. Our model provides a new idea for the accurate orientation detection of insulators.<\/jats:p>","DOI":"10.3390\/s22249773","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T03:21:52Z","timestamp":1670988112000},"page":"9773","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A New Orientation Detection Method for Tilting Insulators Incorporating Angle Regression and Priori Constraints"],"prefix":"10.3390","volume":"22","author":[{"given":"Jianli","family":"Zhao","sequence":"first","affiliation":[{"name":"Electric Power Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1216-6644","authenticated-orcid":false,"given":"Liangshuai","family":"Liu","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ze","family":"Chen","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanpeng","family":"Ji","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Feng","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"5016408","DOI":"10.1109\/TIM.2021.3112227","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_3","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":"2020","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_4","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":"Zhang","year":"2019","journal-title":"CSEE J. Power Energy Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3522412","DOI":"10.1109\/TIM.2022.3200861","article-title":"An insulator defect detection model in aerial images based on Multiscale Feature Pyramid Network","volume":"71","author":"Hao","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mei, X., Lu, T., Wu, X., and Zhang, B. (2012, January 27\u201329). Insulator surface dirt image detection technology based on improved watershed algorithm. Proceedings of the Asia-Pacific Power and Energy Engineering Conference, Shanghai, China.","DOI":"10.1109\/APPEEC.2012.6307691"},{"key":"ref_7","first-page":"1568","article-title":"Insulator string location method based on airspace morphological consistency characteristics","volume":"37","author":"Zhai","year":"2017","journal-title":"Chin. J. Electr. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, X., An, J., and Chen, F. (2010, January 16\u201317). A method of insulator fault detection from airborne images. Proceedings of the 2010 Second WRI Global Congress on Intelligent Systems, Wuhan, China.","DOI":"10.1109\/GCIS.2010.74"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"94970","DOI":"10.1109\/ACCESS.2021.3071305","article-title":"Insulator anomaly detection method based on few-shot learning","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1109\/TIE.2019.2899555","article-title":"Design of a new vision-based method for the bolts looseness detection in flange connections","volume":"67","author":"Wang","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, Q., Zhao, F., Xu, Z., Wang, J., Liu, K., and Qin, L. (2022, January 25\u201327). Insulator and damage detection and location based on YOLOv5. Proceedings of the 2022 International Conference on Power Energy Systems and Applications (ICoPESA), Singapore.","DOI":"10.1109\/ICoPESA54515.2022.9754476"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5242","DOI":"10.1109\/TII.2021.3123107","article-title":"Arbitrary-oriented detection of insulators in thermal imagery via rotation region network","volume":"18","author":"Zheng","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yao, L., and Yaoyao, Q. (2020, January 6\u20138). Insulator detection dased on GIOU-YOLOv3. Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China.","DOI":"10.1109\/CAC51589.2020.9326959"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, C., Yu, S., Yu, M., Wei, B., Li, B., Li, G., and Huang, W. (2021, January 5\u20138). Adaptive smooth L1 loss: A Better way to regress scene texts with extreme aspect ratios. Proceedings of the 2021 IEEE Symposium on Computers and Communications (ISCC), Athens, Greece.","DOI":"10.1109\/ISCC53001.2021.9631466"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhou, Y., and Li, G. (2020, January 8\u201311). Anomaly detection by using streaming K-means and batch K-means. Proceedings of the 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), Xiamen, China.","DOI":"10.1109\/ICBDA49040.2020.9101212"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1109\/TIV.2022.3145035","article-title":"Parallel vision for long-tail regularization: Initial results from IVFC autonomous driving testing","volume":"7","author":"Wang","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shuai, X., Shen, Y., Jiang, S., Zhao, Z., Yan, Z., and Xing, G. (2022, January 4\u20136). BalanceFL: Addressing class imbalance in long-tail federated learning. Proceedings of the 2022 21st ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Milano, Italy.","DOI":"10.1109\/IPSN54338.2022.00029"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kalra, A., Stoppi, G., Brown, B., Agarwal, R., and Kadambi, A. (2021, January 11\u201317). Towards rotation invariance in object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00351"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J., Feng, Z., and He, T. (2021). R3Det: Refined single-stage detector with feature refinement for rotating object. arXiv.","DOI":"10.1609\/aaai.v35i4.16426"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). SCRDet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J., Liao, W., Yang, X., Tang, J., and He, T. (2022). SCRDet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing. arXiv.","DOI":"10.1109\/TPAMI.2022.3166956"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9773\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:40:27Z","timestamp":1760146827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9773"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":21,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249773"],"URL":"https:\/\/doi.org\/10.3390\/s22249773","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,12,13]]}}}