{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:17:47Z","timestamp":1770833867689,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB0504504"],"award-info":[{"award-number":["2018YFB0504504"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["AR2203"],"award-info":[{"award-number":["AR2203"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["A2201"],"award-info":[{"award-number":["A2201"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Funded Project of Fundamental Scientific Research Business Expenses of the Chinese Academy of Surveying and Mapping","award":["2018YFB0504504"],"award-info":[{"award-number":["2018YFB0504504"]}]},{"name":"Funded Project of Fundamental Scientific Research Business Expenses of the Chinese Academy of Surveying and Mapping","award":["AR2203"],"award-info":[{"award-number":["AR2203"]}]},{"name":"Funded Project of Fundamental Scientific Research Business Expenses of the Chinese Academy of Surveying and Mapping","award":["A2201"],"award-info":[{"award-number":["A2201"]}]},{"name":"Overall Design of Intelligent Mapping System and Research on Several Technologies","award":["2018YFB0504504"],"award-info":[{"award-number":["2018YFB0504504"]}]},{"name":"Overall Design of Intelligent Mapping System and Research on Several Technologies","award":["AR2203"],"award-info":[{"award-number":["AR2203"]}]},{"name":"Overall Design of Intelligent Mapping System and Research on Several Technologies","award":["A2201"],"award-info":[{"award-number":["A2201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Arc sag is an important parameter in the design and operation and maintenance of transmission lines and is directly related to the safety and reliability of grid operation. The current arc sag measurement method is inefficient and costly, which makes it difficult to meet the engineering demand for fast inspection of transmission lines. In view of this, this paper proposes an automatic spacer bar segmentation algorithm, CM-Mask-RCNN, that combines the CAB attention mechanism and MHSA self-attention mechanism, which automatically extracts the spacer bars and calculates the center coordinates, and combines classical algorithms such as beam method leveling, spatial front rendezvous, and spatial curve fitting, based on UAV inspection video data, to realize arc sag measurement with a low cost and high efficiency. It is experimentally verified that the CM-Mask-RCNN algorithm proposed in this paper achieves an AP index of 73.40% on the self-built dataset, which is better than the Yolact++, U-net, and Mask-RCNN algorithms. In addition, it is also verified that the adopted approach of fusing CAB and MHSA attention mechanisms can effectively improve the segmentation performance of the model, and this combination improves the model performance more significantly compared with other attention mechanisms, with an AP improvement of 2.24%. The algorithm in this paper was used to perform arc sag measurement experiments on 10 different transmission lines, and the measurement errors are all within \u00b12.5%, with an average error of \u22120.11, which verifies the effectiveness of the arc sag measurement method proposed in this paper for transmission lines.<\/jats:p>","DOI":"10.3390\/rs15102533","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T01:05:10Z","timestamp":1683853510000},"page":"2533","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Research on Arc Sag Measurement Methods for Transmission Lines Based on Deep Learning and Photogrammetry Technology"],"prefix":"10.3390","volume":"15","author":[{"given":"Jiang","family":"Song","sequence":"first","affiliation":[{"name":"Chinese Academy of Surveying & Mapping, Beijing 100036, China"},{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123008, China"}]},{"given":"Jianguo","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Geomatics, Liaoning Technical University, Fuxin 123008, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0303-6290","authenticated-orcid":false,"given":"Zhengjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying & Mapping, Beijing 100036, China"}]},{"given":"Yang","family":"Jiao","sequence":"additional","affiliation":[{"name":"China Huaneng Zhalainuoer Coal Industry Co., Ltd., Hulunbuir 021000, China"}]},{"given":"Jiahui","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Ecology and Environment, Xinjiang University, Urumqi 830046, China"}]},{"given":"Yongrong","family":"Li","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying & Mapping, Beijing 100036, China"}]},{"given":"Yiming","family":"Chen","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying & Mapping, Beijing 100036, China"}]},{"given":"Jie","family":"Guo","sequence":"additional","affiliation":[{"name":"China Huaneng Zhalainuoer Coal Industry Co., Ltd., Hulunbuir 021000, China"}]},{"given":"Zhiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"China Huaneng Zhalainuoer Coal Industry Co., Ltd., Hulunbuir 021000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ramachandran, P., and Vittal, V. (2006, January 17\u201319). On-line monitoring of sag in overhead transmission lines with leveled spans. Proceedings of the 2006 38TH Annual North American Power Symposium, NAPS-2006 Proceedings, Carbondale, IL, USA.","DOI":"10.1109\/NAPS.2006.359604"},{"key":"ref_2","unstructured":"Ren, L., Li, H., and Liu, Y. (2012, January 23\u201327). On-line Monitoring and prediction for transmission line sag. Proceedings of the 2012 IEEE International Conference on Condition Monitoring and Diagnosis (IEEE CMD 2012), Bali, Indonesia."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.1109\/TPWRD.2015.2510318","article-title":"Analytic Method to Calculate and Characterize the Sag and Tension of Overhead Lines","volume":"31","author":"Dong","year":"2016","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_4","first-page":"206","article-title":"Real-time Monitoring of Transmission Line Sag","volume":"176","author":"Xu","year":"2007","journal-title":"High Volt. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/15325000390112062","article-title":"Overhead transmission conductor sag: A novel measurement technique and the relation of sag to real time circuit ratings","volume":"31","author":"Heydt","year":"2003","journal-title":"Electr. Power Compon. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wydra, M., Kisala, P., Harasim, D., and Kacejko, P. (2018). Overhead Transmission Line Sag Estimation Using a Simple Optomechanical System with Chirped Fiber Bragg Gratings. Part 1: Preliminary Measurements. Sensors, 18.","DOI":"10.3390\/s18010309"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abraham, A.P., and Ashok, S. (2012, January 7\u20139). Gyro-Accelerometric SAG analysis and Online-Monitoring of Transmission Lines using Line Recon Robot. Proceedings of the 2012 Annual IEEE India Conference (INDICON), Kochi, India.","DOI":"10.1109\/INDCON.2012.6420769"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yan, D., and Liao, Y. (2011, January 4\u20136). Online estimation of power transmission line parameters, temperature and sag. Proceedings of the 2011 North American Power Symposium, Boston, MA, USA.","DOI":"10.1109\/NAPS.2011.6024854"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Oleinikova, I., Mutule, A., and Putnins, M. (2014, January 14). PMU measurements application for transmission line temperature and sag estimation algorithm development. Proceedings of the 2014 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Riga, Latvia.","DOI":"10.1109\/RTUCON.2014.6998196"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2657490","DOI":"10.1109\/TMAG.2017.2657490","article-title":"Estimation of Current and Sag in Overhead Power Transmission Lines With Optimized Magnetic Field Sensor Array Placement","volume":"53","author":"Khawaja","year":"2017","journal-title":"IEEE Trans. Magn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1109\/TPWRD.2013.2264102","article-title":"Noncontact Operation-State Monitoring Technology Based on Magnetic-Field Sensing for Overhead High-Voltage Transmission Lines","volume":"28","author":"Sun","year":"2013","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_12","first-page":"36","article-title":"Research on Sag Measurement System of Transmission Line Based on UAV Image and Catenary Model","volume":"33","author":"Li","year":"2020","journal-title":"Electromech. Inf."},{"key":"ref_13","first-page":"94","article-title":"Analysis and Application of Power Line Sag Assisted by Airborne LiDAR","volume":"7","author":"Zhang","year":"2017","journal-title":"Bull. Surv. Mapp."},{"key":"ref_14","first-page":"58","article-title":"Sag State Estimation of Transmission Line Based on Airborne LiDAR Data","volume":"36","author":"Zhou","year":"2020","journal-title":"Power Sci. Eng."},{"key":"ref_15","first-page":"394","article-title":"Sag Simulation of Airborne Lidar Point Cloud Data Transmission Line","volume":"36","author":"Ma","year":"2019","journal-title":"J. Surv. Mapp. Sci. Technol."},{"key":"ref_16","first-page":"35","article-title":"Transmission Line Sag Detection Method Based on 3D Model Comparison","volume":"37","author":"Du","year":"2021","journal-title":"Power Grid Clean Energy"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sermet, M.Y., Demir, I., and Kucuksari, S. (2018, January 9\u201311). Overhead Power Line Sag Monitoring through Augmented Reality. Proceedings of the 2018 North American Power Symposium (NAPS), Fargo, ND, USA.","DOI":"10.1109\/NAPS.2018.8600565"},{"key":"ref_18","first-page":"108","article-title":"Research on Sag Measurement of Transmission Line Based on Aerial Image","volume":"36","author":"Lu","year":"2019","journal-title":"Comput. Appl. Softw."},{"key":"ref_19","first-page":"115","article-title":"Sag Measurement Method of Transmission Line Based on Aerial Sequence Image","volume":"31","author":"Tong","year":"2011","journal-title":"Chin. J. Electr. Eng."},{"key":"ref_20","first-page":"747","article-title":"Transmission Line Sag Measurement Method Based on Binocular Vision Sparse Point Cloud Reconstruction","volume":"47","author":"Wang","year":"2016","journal-title":"J. Taiyuan Univ. Technol."},{"key":"ref_21","unstructured":"Li, J. (2009). Sag Measurement of Transmission Line Based on Computer Vision. [Master\u2019s Thesis, North China Electric Power University (Hebei)]."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1049\/ipr2.12364","article-title":"Deep coordinate attention network for single image super-resolution","volume":"16","author":"Xie","year":"2022","journal-title":"IET Image Process."},{"key":"ref_24","first-page":"909","article-title":"Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention","volume":"132","author":"Zhang","year":"2022","journal-title":"CMES-Comput. Model. Eng. Sci."},{"key":"ref_25","first-page":"104","article-title":"Speech enhancement method based on the multi-head self-attention mechanism","volume":"47","author":"Chang","year":"2020","journal-title":"J. Xidian Univ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"138603","DOI":"10.1109\/ACCESS.2019.2941964","article-title":"MS-Pointer Network: Abstractive Text Summary Based on Multi-Head Self-Attention","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100013","DOI":"10.1109\/ACCESS.2020.2997871","article-title":"Multi-Head Self-Attention-Based Deep Clustering for Single-Channel Speech Separation","volume":"8","author":"Jin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chang, C.-C., Wang, Y.-P., and Cheng, S.-C. (2021). Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields. Sensors, 21.","DOI":"10.3390\/s21227625"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Degawa, N., Lu, X., and Kimura, A. (2019, January 6\u20139). A performance improvement of Mask R-CNN using region proposal expansion. Proceedings of the International Workshop on Advanced Image Technology (IWAIT) 2019, Singapore.","DOI":"10.1117\/12.2521383"},{"key":"ref_30","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 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Chen, H., and Yu, X. (2021, January 6\u20139). The Segmentation Algorithm of Marine Warship Image Based on Mask RCNN. Proceedings of the Twelfth International Conference on Signal Processing Systems, Shanghai, China.","DOI":"10.1117\/12.2589235"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gong, Y., Yu, X., Ding, Y., Peng, X., Zhao, J., and Han, Z. (2021, January 3\u20138). Effective Fusion Factor in FPN for Tiny Object Detection. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV 2021), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00120"},{"key":"ref_34","first-page":"166","article-title":"Application of Improved Mask RCNN in Offshore Ship Instance Segmentation","volume":"43","author":"Li","year":"2021","journal-title":"Ship Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, C., Wu, Z., Zhou, B., and Lin, S. (2021, January 20\u201325). Instance Localization for Self-supervised Detection Pretraining. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00398"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Krueangsai, A., and Supratid, S. (2022, January 9\u201311). Effects of Shortcut-Level Amount in Lightweight ResNet of ResNet on Object Recognition with Distinct Number of Categories. Proceedings of the 2022 International Electrical Engineering Congress (iEECON), Khon Kaen, Thailand.","DOI":"10.1109\/iEECON53204.2022.9741665"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"18967","DOI":"10.1109\/ACCESS.2018.2814605","article-title":"An Improved ResNet Based on the Adjustable Shortcut Connections","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_38","first-page":"1436","article-title":"Research on Residual Neural Network and Its Application on Medical Image Processing","volume":"48","author":"Zhou","year":"2020","journal-title":"Acta Electron. Sin."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, X., Pan, H., and Li, X. (2020, January 2\u20133). Object detection for rotated and densely arranged objects in aerial images using path aggregated feature pyramid networks. Proceedings of the MIPPR 2019: Pattern Recognition and Computer Vision, Wuhan, China.","DOI":"10.1117\/12.2538090"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wu, Y., Tang, S., Zhang, S., and Ogai, H. (2019). An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving. Appl. Sci., 9.","DOI":"10.3390\/app9204363"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1109\/TPAMI.2020.3014297","article-title":"YOLACT++ Better Real-Time Instance Segmentation","volume":"44","author":"Bolya","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhuang, W., and Shang, D. (2022, January 22\u201324). Light Enhancement Algorithm Optimization for Autonomous Driving Vision in Night Scenes based on YOLACT++. Proceedings of the 2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS), Guangzhou, China.","DOI":"10.1109\/ISPDS56360.2022.9874070"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"014006","DOI":"10.1117\/1.JMI.6.1.014006","article-title":"Recurrent residual U-Net for medical image segmentation","volume":"6","author":"Alom","year":"2019","journal-title":"J. Med. Imaging"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, K., Xu, G., Qian, J., and Ren, C.-X. (2019, January 17\u201320). A Bypass-Based U-Net for Medical Image Segmentation. Proceedings of the Intelligence Science and Big Data Engineering: Visual Data Engineering, PT I, Nanjing, China.","DOI":"10.1007\/978-3-030-36189-1_13"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_48","first-page":"2353","article-title":"Lightweight attention mechanism module based on squeeze and excitation","volume":"42","author":"Lyu","year":"2022","journal-title":"J. Comput. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ovalle-Magallanes, E., Gabriel Avina-Cervantes, J., Cruz-Aceves, I., and Ruiz-Pinales, J. (2022). LRSE-Net: Lightweight Residual Squeeze-and-Excitation Network for Stenosis Detection in X-ray Coronary Angiography. Electronics, 11.","DOI":"10.3390\/electronics11213570"},{"key":"ref_50","unstructured":"Wang, C., Li, B., Jiao, B., Liang, Y., and Cheng, P. (2020). Convolutional Block Attention Module (CBAM)-Based Convolutional Neural Network Rolling Bearing Fault Diagnosis Method, Involves Inputting Detected Rolling Bearing Data Set to Trained CBAM-Based Network to Output Fault Diagnosis Result. (CN111458148-A)."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the Computer Vision\u2013ECCV 2018, PT VII, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Xiang, L., Zhou, Z., Miao, L., and Chen, Q. (2021, January 22\u201324). Signal Recognition Method of X-ray Pulsar Based on CNN and Attention Module CBAM. Proceedings of the 33rd Chinese Control and Decision Conference (CCDC 2021), Kunming, China.","DOI":"10.1109\/CCDC52312.2021.9602569"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2533\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:33:08Z","timestamp":1760124788000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2533"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,11]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102533"],"URL":"https:\/\/doi.org\/10.3390\/rs15102533","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,11]]}}}