{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T04:24:35Z","timestamp":1784521475211,"version":"3.55.0"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271171"],"award-info":[{"award-number":["62271171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["YQJH2023262"],"award-info":[{"award-number":["YQJH2023262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Heilongjiang Province Outstanding Young Teacher Basic Research Support Program","award":["62271171"],"award-info":[{"award-number":["62271171"]}]},{"name":"Heilongjiang Province Outstanding Young Teacher Basic Research Support Program","award":["YQJH2023262"],"award-info":[{"award-number":["YQJH2023262"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming to address the issues of parameter complexity and high computational load in existing fault detection algorithms for transmission lines, which hinder their deployment on devices like drones, this study proposes a novel lightweight model called Leaner YOLOv7-Tiny. The primary goal is to swiftly and accurately detect typical faults in transmission lines from aerial images. This algorithm inherits the ELAN structure from YOLOv7-Tiny network and replaces its backbone with depthwise separable convolutions to reduce model parameters. By integrating the SP attention mechanism, it fuses multi-scale information, capturing features across various scales to enhance small target recognition. Finally, an improved FCIoU Loss function is introduced to balance the contribution of high-quality and low-quality samples to the loss function, expediting model convergence and boosting detection accuracy. Experimental results demonstrate a 20% reduction in model size compared to the original YOLOv7-Tiny algorithm. Detection accuracy for small targets surpasses that of current mainstream lightweight object detection algorithms. This approach holds practical significance for transmission line fault detection.<\/jats:p>","DOI":"10.3390\/s24020565","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T11:37:18Z","timestamp":1705405038000},"page":"565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Lightweight Transmission Line Fault Detection Method Based on Leaner YOLOv7-Tiny"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8565-8690","authenticated-orcid":false,"given":"Qingyan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Measurement-Control and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Measurement-Control and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingguo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Measurement-Control and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1082-114X","authenticated-orcid":false,"given":"Junping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouqiang","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Measurement-Control and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.compag.2010.10.001","article-title":"Scale invariant feature approach for insect monitoring","volume":"75","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1109\/CVPR.2005.177","article-title":"Histograms of oriented gradients for human detection","volume":"Volume 1","author":"Dalal","year":"2005","journal-title":"Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905)"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. TIST"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1006\/inco.1995.1136","article-title":"Boosting a weak learning algorithm by majority","volume":"121","author":"Freund","year":"1995","journal-title":"Inf. Comput."},{"key":"ref_6","unstructured":"Li, H. (2016). Research on Insulator Status Identification Method Based on Sparse Representation. [Master\u2019s Thesis, North China Electric Power University]."},{"key":"ref_7","unstructured":"Yang, H.J. (2017). Crack Detection and Positioning Bracket Inspection of Contact Network Insulators Based on Image Processing. [Master\u2019s Thesis, Southwest Jiaotong University]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/TDEI.2011.5739465","article-title":"A DOST based approach for the condition monitoring of 11 kV distribution line insulators","volume":"18","author":"Chandra","year":"2011","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_9","unstructured":"Chen, H.L. (2022). Research on Insulator Damage Detection Based on Improved Faster R-CNN Algorithm. [Master\u2019s Thesis, Shijiazhuang Tiedao University]."},{"key":"ref_10","first-page":"1","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, M., Wang, J., and Li, B. (2022). ARG-Mask RCNN: An Infrared Insulator Fault-Detection Network Based on Improved Mask RCNN. Sensors, 22.","DOI":"10.3390\/s22134720"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_13","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_14","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_15","first-page":"667","article-title":"Multi-Class Defect Detection Method for Transmission Line Based on TR-YOLOv5","volume":"44","author":"Hao","year":"2023","journal-title":"J. Graph."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_17","first-page":"5084","article-title":"Insulator Defect Detection and Fault Warning Method for Transmission Lines Based on Flexible YOLOv7","volume":"49","author":"Song","year":"2023","journal-title":"High Volt. Eng."},{"key":"ref_18","unstructured":"Cui, C., Gao, T., Wei, S., Du, Y., Guo, R., Dong, S., Lu, B., Zhou, Y., Lv, X., and Liu, Q. (2021). PP-LCNet: A lightweight CPU convolutional neural network. arXiv."},{"key":"ref_19","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"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":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_23","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","article-title":"Focal and efficient IOU loss for accurate bounding box regression","volume":"506","author":"Zhang","year":"2022","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/565\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:48:21Z","timestamp":1760104101000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/565"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,16]]},"references-count":24,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020565"],"URL":"https:\/\/doi.org\/10.3390\/s24020565","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,16]]}}}