{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T17:55:16Z","timestamp":1785779716989,"version":"3.56.0"},"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Power transmission insulators develop defects from sustained high-voltage stress, thermal cycling, and environmental contamination, with undetected degradation progressively leading to self-explosion, flashover, and mechanical failure. Unmanned aerial vehicle (UAV)-based defect detection faces persistent challenges from small object scales, complex background clutter, and multi-scale feature misalignment in lightweight deployable architectures. This paper proposes MLE-YOLOv11n (Multi-scale Layer aggregation and context-aware feature fusion Enhanced YOLOv11n), an enhanced detection framework built upon YOLOv11n that introduces three targeted module substitutions at distinct architectural stages. The Spatial Pyramid Pooling with Efficient Layer Aggregation Network (SPPELAN) replaces the standard Spatial Pyramid Pooling Fast (SPPF) module at the backbone terminus, preserving intermediate pooling representations at each aggregation depth to enrich multi-scale semantic features entering the neck. The Multi-branch Feature Context-Aware (MFCA) attention module replaces equal-weight concatenation at each Path Aggregation Network (PANet) neck fusion node, integrating parallel multi-branch feature enhancement with asymmetric kernels, learnable per-channel adaptive weighting, and cross-region spatial context modeling to improve neck feature discriminability. The Mamba-based Local-Long Attention (MLLA) block replaces the standard convolutional prediction unit at the P4-scale detection head, achieving linear-complexity\n                    <jats:inline-formula id=\"pone.0354898.e001\">\n                      <jats:alternatives>\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" id=\"pone.0354898.e001g\" mimetype=\"image\" position=\"anchor\" xlink:href=\"info:doi\/10.1371\/journal.pone.0354898.e001\" xlink:type=\"simple\"\/>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" id=\"M1\">\n                          <mml:mrow>\n                            <mml:mi>\ud835\udcaa<\/mml:mi>\n                            <mml:mo stretchy=\"false\">(<\/mml:mo>\n                            <mml:mi>N<\/mml:mi>\n                            <mml:mo stretchy=\"false\">)<\/mml:mo>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    global structural consistency verification through window-partitioned attention with an Irregular Serpentine Scan, suppressing false positives from background structures visually similar to genuine defects. Evaluated on two public benchmarks, MLE-YOLOv11n attains 92.1% mean Average Precision at IoU threshold 0.50 (mAP@50) on the China Power Line Insulator Dataset (CPLID) and 95.3% mAP@50 on the Insulator Defect Image Dataset (IDID), representing gains of 4.4 and 5.2 percentage points over baseline YOLOv11n respectively, with the largest improvements concentrated in rare and structurally complex defect categories. The framework introduces only 10.8% additional parameters (2.87\u2009M total) at 7.2\u2009GFLOPs while maintaining 85 frames per second (FPS) inference speed, demonstrating a competitive accuracy-efficiency trade-off for UAV-based power grid inspection, with inference speed and parameter count suitable for further evaluation on resource-constrained edge platforms in future deployment studies.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0354898","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T17:25:27Z","timestamp":1785777927000},"page":"e0354898","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["MLE-YOLOv11n: Multi-scale layer aggregation and context-aware feature fusion for insulator defect detection in aerial imagery"],"prefix":"10.1371","volume":"21","author":[{"given":"Yuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanzhi","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinxian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongxiu","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5627-2215","authenticated-orcid":true,"given":"Qiqi","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"issue":"16","key":"pone.0354898.ref001","doi-asserted-by":"crossref","first-page":"3541","DOI":"10.1049\/gtd2.12916","article-title":"Insulator defect detection with deep learning: A survey","volume":"17","author":"Y Liu","year":"2023","journal-title":"IET Gen Trans Dist"},{"issue":"4","key":"pone.0354898.ref002","doi-asserted-by":"crossref","first-page":"265","DOI":"10.3390\/drones9040265","article-title":"State-of-the-Art Review on the Application of Unmanned Aerial Vehicles (UAVs) in Power Line Inspections: Current Innovations, Trends, and Future Prospects","volume":"9","author":"B Mendu","year":"2025","journal-title":"Drones"},{"key":"pone.0354898.ref003","doi-asserted-by":"crossref","first-page":"125507","DOI":"10.1016\/j.apenergy.2025.125507","article-title":"Deep learning in automated power line inspection: A review","volume":"385","author":"MdAA Faisal","year":"2025","journal-title":"Appl Energy"},{"issue":"6","key":"pone.0354898.ref004","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.3390\/electronics14061201","article-title":"Improved YOLO11 Algorithm for Insulator Defect Detection in Power Distribution Lines","volume":"14","author":"Y Ji","year":"2025","journal-title":"Electronics"},{"issue":"2","key":"pone.0354898.ref005","article-title":"UAV inspection insulator defect detection method based on dynamic adaptation improved YOLOv8","volume":"22","author":"C Hu","year":"2025","journal-title":"J Real-Time Image Proc"},{"key":"pone.0354898.ref006","first-page":"1","article-title":"LiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection","volume":"73","author":"D Li","year":"2024","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"pone.0354898.ref007","doi-asserted-by":"crossref","first-page":"35426","DOI":"10.1038\/s41598-025-19059-7","article-title":"RSP-YOLOv11n multi-module optimized algorithm for insulator defect detection in UAV images","volume":"15","author":"B Zheng","year":"2025","journal-title":"Sci Rep"},{"key":"pone.0354898.ref008","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3298424","article-title":"A small-sized defect detection method for overhead transmission lines based on convolutional neural networks","volume":"72","author":"Q Fu","year":"2023","journal-title":"IEEE Trans Instrument Measur"},{"key":"pone.0354898.ref009","unstructured":"Zhu X, Su W, Lu L. Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint. 2020. doi: 10.48550\/arXiv.2010.04159"},{"issue":"9","key":"pone.0354898.ref010","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.3390\/electronics14091828","article-title":"YOLOv8-IDX: Optimized Deep Learning Model for Transmission Line Insulator-Defect Detection","volume":"14","author":"U Farooq","year":"2025","journal-title":"Electronics"},{"issue":"11","key":"pone.0354898.ref011","doi-asserted-by":"crossref","first-page":"2552","DOI":"10.3390\/pr12112552","article-title":"Insulator-YOLO: Transmission Line Insulator Risk Identification Based on Improved YOLOv5","volume":"12","author":"N Zhang","year":"2024","journal-title":"Processes"},{"issue":"6","key":"pone.0354898.ref012","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":"S Ren","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pone.0354898.ref013","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N. Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 6154\u201362.","DOI":"10.1109\/CVPR.2018.00644"},{"issue":"9","key":"pone.0354898.ref014","doi-asserted-by":"crossref","first-page":"6951","DOI":"10.1007\/s00521-025-11011-0","article-title":"An improved transfer learning model for detection of insulator defects in power transmission lines","volume":"37","author":"V Pradeep","year":"2025","journal-title":"Neural Comput Appl"},{"key":"pone.0354898.ref015","doi-asserted-by":"crossref","first-page":"117410","DOI":"10.1016\/j.measurement.2025.117410","article-title":"A insulator defect detection network based on improved YOLOv7 for UAV aerial images","volume":"253","author":"X You","year":"2025","journal-title":"Measurement"},{"issue":"4","key":"pone.0354898.ref016","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"J Terven","year":"2023","journal-title":"MAKE"},{"issue":"1","key":"pone.0354898.ref017","doi-asserted-by":"crossref","DOI":"10.1007\/s11554-023-01401-9","article-title":"Research on improved YOLOv8 algorithm for insulator defect detection","volume":"21","author":"L Zhang","year":"2024","journal-title":"J Real-Time Image Proc"},{"issue":"2","key":"pone.0354898.ref018","article-title":"Real-time detection method of intelligent classification and defect of transmission line insulator based on LightWeight-YOLOv8n network","volume":"22","author":"G Tan","year":"2025","journal-title":"J Real-Time Image Proc"},{"key":"pone.0354898.ref019","unstructured":"Khanam R, Hussain M. Yolov11: An overview of the key architectural enhancements. arXiv preprint. 2024. doi: 10.48550\/arXiv.2410.17725"},{"issue":"5","key":"pone.0354898.ref020","article-title":"FIAEPI-KD: A novel knowledge distillation approach for precise detection of missing insulators in transmission lines","volume":"20","author":"H Cui","year":"2025","journal-title":"PLoS One"},{"key":"pone.0354898.ref021","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, et al. Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 7794\u2013803.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"pone.0354898.ref022","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2021. p. 9992\u201310002.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"pone.0354898.ref023","doi-asserted-by":"crossref","unstructured":"Xu J, Liao H, Li K, et al. Multi-scale feature fusion transformer with hybrid attention for insulator defect detection. IEEE Trans Instrument Measur. 2025.","DOI":"10.1109\/TIM.2025.3568984"},{"issue":"4","key":"pone.0354898.ref024","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1049\/ipr2.13009","article-title":"DGW\u2010YOLOv8: A small insulator target detection algorithm based on deformable attention backbone and WIoU loss function","volume":"18","author":"D Hu","year":"2023","journal-title":"IET Image Process"},{"key":"pone.0354898.ref025","doi-asserted-by":"crossref","unstructured":"Han D, Wang Z, Xia Z, Han Y, Pu Y, Ge C, et al. Demystify Mamba in Vision: A Linear Attention Perspective. In: Advances in Neural Information Processing Systems 37. 2024. p. 127181\u2013203.","DOI":"10.52202\/079017-4039"},{"issue":"8","key":"pone.0354898.ref026","doi-asserted-by":"crossref","first-page":"8205","DOI":"10.1609\/aaai.v39i8.32885","article-title":"Mamba YOLO: A Simple Baseline for Object Detection with State Space Model","volume":"39","author":"Z Wang","year":"2025","journal-title":"AAAI"},{"key":"pone.0354898.ref027","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p. 7132\u201341.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"pone.0354898.ref028","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, et al. Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV). 2018. p. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"pone.0354898.ref029","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2020. p. 11534\u201342.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"pone.0354898.ref030","first-page":"1","article-title":"Local to global: A sparse transformer-based small object detector for remote sensing images","volume":"63","author":"Z Li","year":"2025","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"pone.0354898.ref031","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Dollar P. Focal Loss for Dense Object Detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). 2017. p. 2999\u20133007.","DOI":"10.1109\/ICCV.2017.324"},{"key":"pone.0354898.ref032","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV. Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020. p. 10781\u201390.","DOI":"10.1109\/CVPR42600.2020.01079"},{"issue":"2","key":"pone.0354898.ref033","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/s00530-025-02152-2","article-title":"YOLO-TCS: an enhanced multi-scale network for traffic sign detection integrating multi-level feature fusion and attention","volume":"32","author":"X Yu","year":"2026","journal-title":"Multimedia Syst"},{"issue":"4","key":"pone.0354898.ref034","doi-asserted-by":"crossref","first-page":"5900","DOI":"10.1109\/TNNLS.2024.3389454","article-title":"Multiscale Deep Learning for Detection and Recognition: A Comprehensive Survey","volume":"36","author":"L Jiao","year":"2024","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"pone.0354898.ref035","unstructured":"Howard AG, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint. 2017. doi: 10.48550\/arXiv.1704.04861"},{"key":"pone.0354898.ref036","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p. 6848\u201356.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"pone.0354898.ref037","doi-asserted-by":"crossref","first-page":"124551","DOI":"10.1016\/j.eswa.2024.124551","article-title":"Multi-task learning for hand heat trace time estimation and identity recognition","volume":"255","author":"X Yu","year":"2024","journal-title":"Exp Syst Appl"},{"key":"pone.0354898.ref038","doi-asserted-by":"crossref","first-page":"105223","DOI":"10.1016\/j.infrared.2024.105223","article-title":"Deep soft threshold feature separation network for infrared handprint identity recognition and time estimation","volume":"138","author":"X Yu","year":"2024","journal-title":"Infrared Phys Technol"},{"key":"pone.0354898.ref039","doi-asserted-by":"crossref","unstructured":"Wang CY, Yeh IH, Mark Liao HY. Yolov9: Learning what you want to learn using programmable gradient information. In: European conference on computer vision. Cham: Springer Nature Switzerland; 2024. p. 1\u201321.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"pone.0354898.ref040","unstructured":"Raimundo A. Insulator data set-Chinese power line insulator dataset (CPLID). IEEE Dataport. 2020."},{"key":"pone.0354898.ref041","article-title":"Insulator defect detection","volume":"10","author":"D Lewis","year":"2021","journal-title":"IEEE Dataport"},{"key":"pone.0354898.ref042","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"A Paszke","year":"2019","journal-title":"Adv Neural Inform Process Syst"},{"key":"pone.0354898.ref043","doi-asserted-by":"crossref","unstructured":"Zhao Y, Lv W, Xu S, et al. Detrs beat yolos on real-time object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2024. p. 16965\u201374.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"pone.0354898.ref044","unstructured":"Bochkovskiy A, Wang CY, Liao HYM. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint. 2020. doi: 10.48550\/arXiv.2004.10934"},{"key":"pone.0354898.ref045","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector. In: European conference on computer vision. Cham: Springer International Publishing; 2016. p. 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"pone.0354898.ref046","unstructured":"Xu X, Jiang Y, Chen W. Damo-yolo: A report on real-time object detection design. arXiv preprint. 2022. doi: 10.48550\/arXiv.2211.15444"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0354898","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T17:25:37Z","timestamp":1785777937000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0354898"}},"subtitle":[],"editor":[{"given":"Lianmeng","family":"Jiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,8,3]]},"references-count":46,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8,3]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0354898","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,3]]}}}