{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T16:43:50Z","timestamp":1790181830516,"version":"4.1.0"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T00:00:00Z","timestamp":1768780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned Aerial Vehicle (UAV)-based inspection is crucial for the maintenance and monitoring of high-voltage transmission lines, but detecting small objects in inspection images presents significant challenges, especially under complex backgrounds and varying lighting. These challenges are particularly evident when detecting the wire features of optical fiber composite overhead ground wire and conventional ground wires. Optical fiber composite overhead ground wire (OPGW) is a specialized cable designed to replace conventional shield wires on power utility towers. It contains one or more optical fibers housed in a protective tube, surrounded by layers of aluminum-clad steel and\/or aluminum alloy wires, ensuring robust mechanical strength for grounding and high-bandwidth capabilities for remote sensing and control. Existing detection methods often struggle with low accuracy, insufficient performance, and high computational demands when dealing with small objects. To address these issues, this paper proposes an energy-efficient OPGW feature detection model for UAV-based inspection. The model incorporates a Feature Enhancement Module (FEM) to replace the C3K2 module in the sixth layer of the YOLO11 backbone, improving multi-scale feature extraction. A P2 shallow detection head is added to enhance the perception of small and edge features. Additionally, the traditional Intersection over Union (IoU) loss is replaced with Normalized Wasserstein Distance (NWD) loss function, which improves boundary regression accuracy for small objects. Experimental results show that the proposed method achieves a mAP50 of 78.3% and mAP50\u201395 of 52.0%, surpassing the baseline by 2.3% and 1.1%, respectively. The proposed model offers the advantages of high detection accuracy and low computational resource requirements, providing a practical solution for sustainable UAV-based inspections.<\/jats:p>","DOI":"10.3390\/s26020658","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T14:58:54Z","timestamp":1768834734000},"page":"658","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Energy-Efficient Automated Detection of OPGW Features for Sustainable UAV-Based Inspection"],"prefix":"10.3390","volume":"26","author":[{"given":"Xiaoling","family":"Yan","sequence":"first","affiliation":[{"name":"Guangzhou Electric Power Design Institute, Guangzhou 510630, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wuxing","family":"Mao","sequence":"additional","affiliation":[{"name":"Guangzhou Electric Power Design Institute, Guangzhou 510630, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Li","sequence":"additional","affiliation":[{"name":"Guangzhou Electric Power Design Institute, Guangzhou 510630, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0807-1036","authenticated-orcid":false,"given":"Ruiming","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangzhou Electric Power Design Institute, Guangzhou 510630, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi","family":"Ye","sequence":"additional","affiliation":[{"name":"Guangzhou Electric Power Design Institute, Guangzhou 510630, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faguang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheyu","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JPETS.2018.2881429","article-title":"Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning","volume":"6","author":"Nguyen","year":"2019","journal-title":"IEEE Power Energy Technol. Syst. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Menendez, O.A., Perez, M., and Auat Cheein, F.A. (2016, January 19\u201321). Vision based inspection of transmission lines using unmanned aerial vehicles. Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Baden-Baden, Germany.","DOI":"10.1109\/MFI.2016.7849523"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Luque-Vega, L.F., Castillo-Toledo, B., Loukianov, A., and Gonzalez-Jimenez, L.E. (2014, January 13\u201316). Power line inspection via an unmanned aerial system based on the quadrotor helicopter. Proceedings of the MELECON 2014\u20142014 17th IEEE Mediterranean Electrotechnical Conference, Beirut, Lebanon.","DOI":"10.1109\/MELCON.2014.6820566"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/TPWRD.2009.2035427","article-title":"A Survey of Mobile Robots for Distribution Power Line Inspection","volume":"25","author":"Katrasnik","year":"2010","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_5","first-page":"5013312","article-title":"Hybrid Knowledge R-CNN for Transmission Line Multifitting Detection","volume":"45","author":"Zhai","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"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 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot Multibox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_8","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_9","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2025, October 15). YOLO by Ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_10","unstructured":"Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y.M. (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_11","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., and Han, J. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_12","unstructured":"Jocher, G. (2025, November 02). YOLO11 by Ultralytics. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Faisal, M.A.A., Mecheter, I., Qiblawey, Y., Fernandez, J.H., Chowdhury, M.E., and Kiranyaz, S. (2025). Deep Learning in Automated Power Line Inspection: A Review. arXiv.","DOI":"10.1016\/j.apenergy.2025.125507"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rong, S., He, L., Atici, S.F., and Cetin, A.E. (2025). Advanced YOLO-based Real-Time Power Line Detection for Vegetation Management. arXiv.","DOI":"10.1109\/TPWRD.2025.3578274"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Barros, P., Sequeira, R., Silva, P., and Soeiro, A. (2024). UAV Visual and Thermographic Power Line Detection Using Deep Learning. Sensors, 24.","DOI":"10.3390\/s24175678"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Zhang, Z., and Wang, L. (2024). YOLOv11-Based UAV Foreign Object Detection for Power Transmission Lines. Electronics, 14.","DOI":"10.3390\/electronics14183577"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 13\u201320). Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_19","first-page":"1792","article-title":"Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions","volume":"36","author":"Liu","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s11760-025-03868-4","article-title":"Improved YOLOv7 Algorithm Incorporating InceptionNeXt and Attention Mechanism for Vehicle Detection Under Adverse Lighting Conditions","volume":"19","author":"Guo","year":"2025","journal-title":"Signal Image Video Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s40747-024-01679-7","article-title":"The Algorithm for Foggy Weather Target Detection Based on YOLOv5 in Complex Scenes","volume":"11","author":"Liu","year":"2025","journal-title":"Complex Intell. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ogino, Y., Shoji, Y., Toizumi, T., and Ito, A. (2024). ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing. arXiv.","DOI":"10.1109\/WACV61041.2025.00833"},{"key":"ref_23","first-page":"5611215","article-title":"FFCA-YOLO for Small Object Detection in Remote Sensing Images","volume":"62","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ma, Z., Wang, C., Chen, C., Chen, J., and Zheng, G. (2025). DDF-YOLO: A Small Target Detection Model Using Multi-Scale Dynamic Feature Fusion for UAV Aerial Photography. Aerospace, 12.","DOI":"10.3390\/aerospace12100920"},{"key":"ref_25","unstructured":"Wang, J., Xu, C., Yang, W., and Yu, L. (2021). A Normalized Gaussian Wasserstein Distance for Tiny Object Detection. arXiv."},{"key":"ref_26","unstructured":"Ruder, S. (2016). An Overview of Gradient Descent Optimization Algorithms. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 17\u201321). DETRS Beat YOLOs on Real-Time Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, K., Gan, Z., and Zhu, G.-N. (2025). UAV-DETR: Efficient End-to-End Object Detection for Unmanned Aerial Vehicle Imagery. arXiv.","DOI":"10.1109\/IROS60139.2025.11246176"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/658\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T15:01:41Z","timestamp":1768834901000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/2\/658"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,19]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26020658"],"URL":"https:\/\/doi.org\/10.3390\/s26020658","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,19]]}}}