{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T12:48:16Z","timestamp":1770122896906,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Hubei Province","award":["2022CFA007"],"award-info":[{"award-number":["2022CFA007"]}]},{"name":"the Hubei Provincial Central Guidance Local Science and Technology Development Project","award":["2023EGA027"],"award-info":[{"award-number":["2023EGA027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Transmission lines in complex outdoor environments often suffer external damage in construction areas, severely affecting the stability of power systems. Traditional manual detection methods have problems of low efficiency and poor real-time performance. In deep learning-based detection methods, standard convolution has a large parameter count and computational complexity, making it difficult to deploy on edge devices; while lightweight depthwise separable convolution offers low computational cost, it suffers from insufficient feature extraction capability. This limitation stems from its independent processing of each channel\u2019s information, making it unable to simultaneously meet the practical requirements for both lightweight design and high detection accuracy in transmission line monitoring applications. To address the above problems, this study proposes LFRE-YOLO, a lightweight external damage detection algorithm for transmission lines based on YOLOv10n. This study proposes LFRE-YOLO, a lightweight external damage detection algorithm based on YOLOv10n. First, we design a lightweight feature reuse and enhancement convolution (LFREConv) that overcomes the limitations of traditional depthwise separable convolution through cascaded dual depthwise convolution structure and residual connection mechanisms, significantly expanding the effective receptive field with minimal parameter increment and compensating for information loss caused by independent channel processing in depthwise convolution through feature reuse strategies. Second, based on LFREConv, we propose an efficient lightweight feature extraction module (LFREBlock) that achieves cross-channel information interaction enhancement and channel importance modeling. Additionally, we propose a lightweight feature reuse and enhancement detection head (LFRE-Head) that applies LFREConv to the regression branch, achieving comprehensive lightweight design of the detection head while maintaining spatial localization accuracy. Finally, we employ layer-adaptive magnitude-based pruning (LAMP) to prune the trained model, further optimizing the network structure through layer-wise adaptive pruning. Experimental results demonstrate significant improvements over YOLOv10n baseline: mAP50 increased from 92.0% to 94.1%, mAP50-95 improved from 66.2% to 70.2%, while reducing parameters from 2.27 M to 0.99 M, computational complexity from 6.5 G to 3.1 G, and achieving 86.9 FPS inference speed, making it suitable for resource-constrained edge computing environments.<\/jats:p>","DOI":"10.3390\/info16121035","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:25:16Z","timestamp":1764239116000},"page":"1035","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LFRE-YOLO: Lightweight Edge Computing Algorithm for Detecting External-Damage Objects on Transmission Lines"],"prefix":"10.3390","volume":"16","author":[{"given":"Min","family":"Liu","sequence":"first","affiliation":[{"name":"Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3979-5547","authenticated-orcid":false,"given":"Benhui","family":"Wu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7873-1132","authenticated-orcid":false,"given":"Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jia, H., Han, Z., Xu, X., Wu, P., Qin, R., Jin, Y., Wang, X., and Huang, W. (2022, January 17\u201318). A Background Reasoning Framework for External Force Damage Detection in Distribution Network. Proceedings of the Annual Conference of China Electrotechnical Society, Beijing, China.","DOI":"10.1007\/978-981-99-0408-2_84"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108277","DOI":"10.1016\/j.ijepes.2022.108277","article-title":"Key target and defect detection of high-voltage power transmission lines with deep learning","volume":"142","author":"Liu","year":"2022","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7068349","DOI":"10.1155\/2018\/7068349","article-title":"Deep learning for computer vision: A brief review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1007\/s11831-020-09504-3","article-title":"Computer vision techniques in construction: A critical review","volume":"28","author":"Xu","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105478","DOI":"10.1016\/j.engappai.2022.105478","article-title":"Computer vision framework for crack detection of civil infrastructure\u2014A review","volume":"117","author":"Ai","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_7","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_8","unstructured":"Yu, G., Chang, Q., Lv, W., Xu, C., Cui, C., Ji, W., Dang, Q., Deng, K., Wang, G., and Du, Y. (2021). PP-PicoDet: A better real-time object detector on mobile devices. arXiv."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_12","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 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Song, G., Liu, Y., and Wang, X. (2020, January 13\u201319). Revisiting the sibling head in object detector. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01158"},{"key":"ref_14","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2025, November 23). YOLOv8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_15","first-page":"107984","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xiang, X., Lv, N., Guo, X., Wang, S., and El Saddik, A. (2018). Engineering vehicles detection based on modified faster R-CNN for power grid surveillance. Sensors, 18.","DOI":"10.3390\/s18072258"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qu, L., Liu, K., He, Q., Tang, J., and Liang, D. (2018, January 23\u201326). External damage risk detection of transmission lines using e-ohem enhanced faster r-cnn. Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Guangzhou, China.","DOI":"10.1007\/978-3-030-03341-5_22"},{"key":"ref_18","first-page":"460","article-title":"Detection of transmission line against external force damage based on improved YOLOv3","volume":"35","author":"Liu","year":"2020","journal-title":"Int. J. Robot. Autom"},{"key":"ref_19","first-page":"283","article-title":"A real-time detection method of safety hazards in transmission lines based on YOLOv5s","volume":"Volume 12456","author":"Li","year":"2022","journal-title":"Proceedings of the International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022)"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zou, H., Ye, Z., Sun, J., Chen, J., Yang, Q., and Chai, Y. (2024). Research on detection of transmission line corridor external force object containing random feature targets. Front. Energy Res., 12.","DOI":"10.3389\/fenrg.2024.1295830"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"123983","DOI":"10.1016\/j.apenergy.2024.123983","article-title":"Intelligent detection method with 3D ranging for external force damage monitoring of power transmission lines","volume":"374","author":"Li","year":"2024","journal-title":"Appl. Energy"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zou, H., Yang, J., Sun, J., Yang, C., Luo, Y., and Chen, J. (2024). Detection method of external damage hazards in transmission line corridors based on YOLO-LSDW. Energies, 17.","DOI":"10.3390\/en17174483"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1016\/j.egyr.2025.06.052","article-title":"A lightweight RKM-YOLO algorithm for transmission line fault inspection","volume":"14","author":"Li","year":"2025","journal-title":"Energy Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, Y., Li, X., Yuan, B., and Wang, M. (2025). SCI-YOLO11: An improved defect detection algorithm for transmission line insulators based on YOLO11. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0322561"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ji, Y., Ma, T., Shen, H., Feng, H., Zhang, Z., Li, D., and He, Y. (2025). Transmission Line Defect Detection Algorithm Based on Improved YOLOv12. Electronics, 14.","DOI":"10.3390\/electronics14122432"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13\u201319). Ghostnet: More features from cheap operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_27","unstructured":"Han, S., Pool, J., Tran, J., and Dally, W. (2015, January 7\u201312). Learning both weights and connections for efficient neural network. Proceedings of the 29th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_28","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H.P. (2016). Pruning filters for efficient convnets. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., and Sun, J. (2017, January 22\u201329). Channel pruning for accelerating very deep neural networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, J.H., Wu, J., and Lin, W. (2017, January 22\u201329). Thinet: A filter level pruning method for deep neural network compression. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref_31","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_32","unstructured":"Lee, J., Park, S., Mo, S., Ahn, S., and Shin, J. (2020). Layer-adaptive sparsity for the magnitude-based pruning. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_34","unstructured":"Tan, M., and Le, Q.V. (2019). Mixconv: Mixed depthwise convolutional kernels. arXiv."},{"key":"ref_35","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_36","unstructured":"Ultralytics (2023, May 07). Ultralytics\/Yolov5: v7.0\u2014YOLOv5 SOTA Realtime Instance Segmentation. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_37","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv."},{"key":"ref_38","unstructured":"Wang, C., He, W., Nie, Y., Guo, J., Liu, C., Han, K., and Wang, Y. (2023). Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism. arXiv 2023. arXiv."},{"key":"ref_39","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_40","unstructured":"Wang, C.Y., Yeh, I.H., and Mark Liao, H.Y. (October, January 29). Yolov9: Learning what you want to learn using programmable gradient information. Proceedings of the European Conference on Computer Vision, Milan, Italy."},{"key":"ref_41","unstructured":"Khanam, R., and Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1035\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:49:18Z","timestamp":1764240558000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1035"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,27]]},"references-count":41,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["info16121035"],"URL":"https:\/\/doi.org\/10.3390\/info16121035","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,27]]}}}