Review of deep learning-based foreign object intrusion detection for transmission lines via UAV aerial photography

Liu Chuanyang; Wu Yiquan; Liu Jingjing

doi:10.11959/j.issn.2096-6652.ZNKX26042

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Review of deep learning-based foreign object intrusion detection for transmission lines via UAV aerial photography

Surveys and Prospectives | 更新时间：2026-07-29

- Review of deep learning-based foreign object intrusion detection for transmission lines via UAV aerial photography
- Chinese Journal of Intelligent Science and Technology Vol. 8, Issue 2, Pages: 149-168(2026)
- 作者机构：
  
  1.南京航空航天大学电子信息工程学院，江苏南京 211106
  2.池州学院机电工程学院，安徽池州 247000
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61573183);The Key Project of Anhui Provincial Universities Natural Science Research(2025AHGXZK30826;KYJG002)
- DOI：10.11959/j.issn.2096-6652.ZNKX26042
  CLC： TP391.41;TM930.1
- Received：07 April 2026，
  
  Revised：2026-06-11，
  
  Accepted：27 June 2026，
  
  Online First：27 July 2026，
  
  Published：15 June 2026
- 稿件说明：
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刘传洋,吴一全,刘景景.基于深度学习的无人机航拍输电线路侵入异物检测综述[J].智能科学与技术学报,2026,08(02):149-168.

Liu Chuanyang,Wu Yiquan,Liu Jingjing.Review of deep learning-based foreign object intrusion detection for transmission lines via UAV aerial photography[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):149-168.
刘传洋,吴一全,刘景景.基于深度学习的无人机航拍输电线路侵入异物检测综述[J].智能科学与技术学报,2026,08(02):149-168. DOI： 10.11959/j.issn.2096-6652.ZNKX26042.

Liu Chuanyang,Wu Yiquan,Liu Jingjing.Review of deep learning-based foreign object intrusion detection for transmission lines via UAV aerial photography[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):149-168. DOI： 10.11959/j.issn.2096-6652.ZNKX26042.

摘要

基于无人机航拍图像和深度学习算法实现输电线路侵入异物的精准有效检测，有助于快速排查输电线路潜在安全隐患，进而提升电网精益化运维能力。综述了基于深度学习的无人机航拍输电线路侵入异物检测方法的研究进展。首先，从Faster R-CNN、SSD、YOLO系列算法及其他衍生算法4个维度，系统梳理了基于深度学习的无人机航拍图像异物检测方法。其次，阐释了无人机航拍图像下输电线路侵入异物检测的改进模型，分别阐明了注意力机制、多尺度特征融合、轻量化网络等技术在输电线路侵入异物检测中的应用路径及其带来的性能优化效果。接着，剖析了基于传统Transformer架构的DETR类异物检测模型，以及面向电力行业的Transformer大模型及其工程应用。最后，针对现有无人机航拍输电线路侵入异物智能检测面临的挑战，对下一步研究工作进行了展望。

Abstract

Efficient and accurate detection of foreign object intrusion on transmission lines based on UAV aerial images and deep learning algorithms enables rapid identification of potential safety hazards

thereby improving the lean operation and maintenance capability of power grids. The research progress of deep learning-based foreign object intrusion detection methods for transmission lines using UAV aerial images was reviewed. First

deep learning-based foreign object detection methods for UAV aerial images were systematically reviewed from four dimensions: Faster R-CNN

SSD

the YOLO series algorithms

and other derivative algorithms. Second

an improved model for UAV aerial image-based foreign object intrusion detection in transmission lines was elaborated. It illustrated the application strategies and performance optimization effects of core technologies in detection tasks

including attention mechanisms

multi-scale feature fusion

and lightweight networks. Subsequently

the DETR-based foreign object detection models built on the vanilla Transformer architecture were investigated

as well as large Transformer models tailored for the power industry and their engineering applications. Finally

in view of the current challenges in UAV aerial image-based intelligent foreign object detection for transmission lines

future research prospects were proposed.

关键词

Keywords

references

黄新波. 基于图像感知的输电线路智能巡检综述[J]. 高电压技术, 2024, 50(5): 1826-1841.

Huang X B. Survey of intelligent inspection based on image perception[J]. High Voltage Engineering, 2024, 50(5): 1826-1841.

田庆, 胡蓉, 李佐勇, 等. 基于SE-YOLOv5s的绝缘子检测[J]. 智能科学与技术学报, 2021, 3(3): 312-321.

Tian Q, Hu R, Li Z Y, et al. Insulator detection based on SE-YOLOv5s[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 312-321.

张焕龙, 齐企业, 张杰, 等. 基于改进YOLOv5的输电线路鸟巢检测方法研究[J]. 电力系统保护与控制, 2023, 51(2): 151-159.

Zhang H L, Qi Q Y, Zhang J, et al. Bird nest detection method for transmission lines based on improved YOLOv5[J]. Power System Protection and Control, 2023, 51(2): 151-159.

何植仟, 曹立杰. UAVAI-YOLO: 无人机航拍图像的小目标检测模型[J]. 智能科学与技术学报, 2024, 6(2): 262-271.

He Z Q, Cao L J. UAVAI-YOLO: dense small target detection algorithm based on UAV aerial images[J]. Chinese Journal of Intelligent Science and Technology, 2024, 6(2): 262-271.

付晶, 谈家英, 周立玮, 等. 输电线路无人机巡检图像故障智能识别方法分析[J]. 高电压技术, 2023, 49(S1): 103-110.

Fu J, Tan J Y, Zhou L W, et al. Analysis of intelligent defect recognition method in UAV inspection images of transmission lines[J]. High Voltage Engineering, 2023, 49(S1): 103-110.

刘传洋, 吴一全, 刘景景. 基于视觉的输电线路金具锈蚀缺陷检测方法研究进展[J]. 仪器仪表学报, 2024, 45(3): 286-305.

Liu C Y, Wu Y Q, Liu J J. Research progress of vision-based rust defect detection methods for metal fittings in transmission lines[J]. Chinese Journal of Scientific Instrument, 2024, 45(3): 286-305.

赵振兵, 蒋志钢, 李延旭, 等. 输电线路部件视觉缺陷检测综述[J]. 中国图象图形学报, 2021, 26(11): 2545-2560.

Zhao Z B, Jiang Z G, Li Y X, et al. Overview of visual defect detection of transmission line components[J]. Journal of Image and Graphics, 2021, 26(11): 2545-2560.

刘志颖, 缪希仁, 陈静, 等. 电力架空线路巡检可见光图像智能处理研究综述[J]. 电网技术, 2020, 44(3): 1057-1069.

Liu Z Y, Miao X R, Chen J, et al. Review of visible image intelligent processing for transmission line inspection[J]. Power System Technology, 2020, 44(3): 1057-1069.

刘开培, 李博强, 秦亮, 等. 深度学习目标检测算法在架空输电线路绝缘子缺陷检测中的应用研究综述[J]. 高电压技术, 2023, 49(9): 3584-3595.

Liu K P, Li B Q, Qin L, et al. Review of application research of deep learning object detection algorithms in insulator defect detection of overhead transmission lines[J]. High Voltage Engineering, 2023, 49(9): 3584-3595.

Luo Y H, Yu X, Yang D S, et al. A survey of intelligent transmission line inspection based on unmanned aerial vehicle[J]. Artificial Intelligence Review, 2023, 56(1): 173-201.

Liu X Y, Miao X R, Jiang H, et al. Data analysis in visual power line inspection: an in-depth review of deep learning for component detection and fault diagnosis[J]. Annual Reviews in Control, 2020, 50: 253-277.

Nguyen V N, Jenssen R, Roverso D. Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning[J]. International Journal of Electrical Power & Energy Systems, 2018, 99: 107-120.

Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2014: 580-587.

Dai J F, Li Y, He K M. R-FCN: object detection via region-based fully convolutional networks[C]//Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS). Massachusetts: MIT Press, 2016: 379-387.

Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

He K M, Gkioxari G, Dollár P, et al. Mask R-CNN[PP]. V3. (2018-01-24)[2026-04-07]. arXiv: arXiv.1703.06870.

Liu W, Anguelov D, Erhan D, et al. SSD: single shot MultiBox detector[C]//Computer Vision-ECCV. Berlin: Springer, 2016: 21-37.

Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2017: 6517-6525.

Redmon J, Farhadi A. YOLOv3: an incremental improvement[PP]. V1. (2018-04-08)[2026-04-07]. arXiv: arXiv.1804.02767.

Bochkovskiy A, Wang C Y, Liao H M. YOLOv4: optimal speed and accuracy of object detection[PP]. V1. (2020-04-23)[2026-04-07]. arXiv: arXiv.2004.10934.

Wang C Y, Bochkovskiy A, Liao H M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[PP]. V1. (2022-07-06)[ 2026-04-07]. arXiv: arXiv.2207.02696.

Girshick R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2015: 1440-1448.

He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//Computer Vision-ECCV. Berlin: Springer, 2014: 346-361.

Chatfield K, Simonyan K, Vedaldi A, et al. Return of the devil in the details: delving deep into convolutional nets[C]//Proceedings of the British Machine Vision Conference. Berlin: Springer, 2014: 1-12.

Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2015: 1-9.

He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 770-778.

Li J, Yan D F, Luan K, et al. Deep learning-based bird's nest detection on transmission lines using UAV imagery[J]. Applied Sciences, 2020, 10(18): 6147.

Xiang X Z, Lv N, Guo X L, et al. Engineering vehicles detection based on modified faster R-CNN for power grid surveillance[J]. Sensors, 2018, 18(7): 2258.

Hao J, Lin S J, Wang L, et al. Research on detection methods of foreign objects and component defects in transmission lines based on deep learning[C]//Proceedings of the 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2). Piscataway: IEEE Press, 2021: 3070-3075.

Zhang F, Fan Y L, Cai T, et al. OTL-classifier: towards imaging processing for future unmanned overhead transmission line maintenance[J]. Electronics, 2019, 8(11): 1270.

路艳巧, 孙翠英, 曹红卫, 等. 基于边缘计算与深度学习的输电设备异物检测方法[J]. 中国电力, 2020, 53(6): 27-33.

Lu Y Q, Sun C Y, Cao H W, et al. Foreign body detection method for transmission equipment based on edge computing and deep learning[J]. Electric Power, 2020, 53(6): 27-33.

马富齐, 王波, 董旭柱, 等. 面向输电线路覆冰厚度辨识的多感受野视觉边缘智能识别方法研究[J]. 电网技术, 2021, 45(6): 2161-2169.

Ma F Q, Wang B, Dong X Z, et al. Receptive field vision edge intelligent recognition for ice thickness identification of transmission line[J]. Power System Technology, 2021, 45(6): 2161-2169.

Zhu J G, Guo Y, Yue F D, et al. A deep learning method to detect foreign objects for inspecting power transmission lines[J]. IEEE Access, 2020, 8: 94065-94075.

严宇平, 杨秋勇, 谢翰阳, 等. 基于改进YOLOv7的输电线路异物检测模型[J]. 南方电网技术, 2024, 18(9): 47-58.

Yan Y P, Yang Q Y, Xie H Y, et al. Foreign object detection model of transmission line based on improved YOLOv7[J]. Southern Power System Technology, 2024, 18(9): 47-58.

王亚茹, 冯利龙, 宋晓轲, 等. TFD-YOLOv8: 一种用于输电线路的异物检测方法[J]. 图学学报, 2024, 45(5): 901-912.

Wang Y R, Feng L L, Song X K, et al. TFD-YOLOv8: a transmission line foreign object detection method[J]. Journal of Graphics, 2024, 45(5): 901-912.

Liu C Y, Wei S F, Zhong S B, et al. YOLO-PowerLite: a lightweight YOLO model for transmission line abnormal target detection[J]. IEEE Access, 2024, 12: 105004-105015.

Li S, Du K, Li Z Y, et al. An improved YOLOv8n network for transmission line intrusion foreign object detection[C]//Proceedings of the 2024 9th Asia Conference on Power and Electrical Engineering (ACPEE). Piscataway: IEEE Press, 2024: 1559-1565.

Yu Y Z, Qiu Z B, Liao H S, et al. A method based on multi-network feature fusion and random forest for foreign objects detection on transmission lines[J]. Applied Sciences, 2022, 12(10): 4982.

李梅, 郭飞, 张立中, 等. 基于TATLNet的输电场景威胁检测[J]. 工程科学学报, 2020, 42(4): 509-515.

Li M, Guo F, Zhang L Z, et al. Threat detection in transmission scenario based on TATLNet[J]. Chinese Journal of Engineering, 2020, 42(4): 509-515.

宋立业, 王诗翱, 刘昕明. 基于改进胶囊网络的电力线巡线异物检测[J]. 电子测量与仪器学报, 2020, 34(12): 49-56.

Song L Y, Wang S A, Liu X M. Detection of foreign objects in power line patrol based on improved capsule network[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(12): 49-56.

Zhang H, Zhao J, Chen Y R, et al. Intelligent bird's nest hazard detection of transmission line based on RetinaNet model[J]. Journal of Physics: Conference Series, 2021, 2025(1): 012235.

Yi J F, Mao J X, Zhang H, et al. PSTL-net: a patchwise self-texture-learning network for transmission line inspection[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 5005714.

Hao Y P, Liang W, Yang L, et al. Methods of image recognition of overhead power line insulators and ice types based on deep weakly-supervised and transfer learning[J]. IET Generation, Transmission & Distribution, 2022, 16(11): 2140-2153.

李利荣, 陈鹏, 张云良, 等. 基于改进CenterNet的输电线路电力器件及异常目标检测[J]. 高电压技术, 2023, 49(11): 4757-4765.

Li L R, Chen P, Zhang Y L, et al. Detection of power devices and abnormal objects in transmission lines based on improved CenterNet[J]. High Voltage Engineering, 2023, 49(11): 4757-4765.

Hu T, Shen L Q, Wu D, et al. Research on transmission line ice-cover segmentation based on improved U-Net and GAN[J]. Electric Power Systems Research, 2023, 221: 109405.

王彤, 朱凌, 范亚洲, 等. 基于改进U-net语义分割遥感影像的线路走廊隐患检测方法[J]. 南方电网技术, 2019, 13(8): 67-73.

Wang T, Zhu L, Fan Y Z, et al. Hidden danger detection method of line corridor in remote sensing image based on improved U-net semantics segmentation[J]. Southern Power System Technology, 2019, 13(8): 67-73.

魏贤哲, 卢武, 赵文彬, 等. 基于改进Mask R-CNN的输电线路防外破目标检测方法研究[J]. 电力系统保护与控制, 2021, 49(23): 155-162.

Wei X Z, Lu W, Zhao W B, et al. Target detection method for external damage of a transmission line based on an improved Mask R-CNN algorithm[J]. Power System Protection and Control, 2021, 49(23): 155-162.

张烨, 高玉菡, 黄新波, 等. 基于Fine Mask RCNN的110~220 kV输电铁塔涉鸟故障识别与评估[J]. 电网技术, 2022, 46(6): 2132-2140.

Zhang Y, Gao Y H, Huang X B, et al. Fault identification and evaluation of tower wading in 110~220 kV transmission line with Fine Mask RCNN[J]. Power System Technology, 2022, 46(6): 2132-2140.

胡广怡, 韩军, 倪源松, 等. 联合单目深度估计的输电导线异物检测方法[J]. 计算机工程与应用, 2025, 61(23): 305-315.

Hu G Y, Han J, Ni Y S, et al. Method for foreign object detection on transmission lines using monocular depth estimation[J]. Computer Engineering and Applications, 2025, 61(23): 305-315.

Wang W K, Meng P, Huang W J, et al. Improved faster R-CNN algorithm for transmission line small target detection[C]//International Conference on Neural Computing for Advanced Applications. Singapore: Springer, 2022: 427-441.

龙乐云, 周腊吾, 刘淑琴, 等. 改进YOLOv5算法下的输电线路外破隐患目标检测研究[J]. 电子测量与仪器学报, 2022, 36(11): 245-253.

Long L Y, Zhou L W, Liu S Q, et al. Identification of hidden damage targets by external forces based on domain adaptation and attention mechanism[J]. Journal of Electronic Measurement and Instrumentation, 2022, 36(11):245-253.

Wu M H, Guo L M, Chen R, et al. Improved YOLOX foreign object detection algorithm for transmission lines[J]. Wireless Communications and Mobile Computing, 2022(10): 1-10.

Wang S S, Tan W W, Yang T F, et al. High-voltage transmission line foreign object and power component defect detection based on improved YOLOv5[J]. Journal of Electrical Engineering & Technology, 2024, 19(1): 851-866.

亢洁, 王勍, 刘文波, 等. 融合CAT-BiFPN与注意力机制的航拍绝缘子多缺陷检测网络[J]. 高电压技术, 2023, 49(8): 3361-3372.

Kang J, Wang Q, Liu W B, et al. Detection model of multi-defect of aerial photo insulator by integrating CAT-BiFPN and attention mechanism[J]. High Voltage Engineering, 2023, 49(8): 3361-3372.

Zhang J, Qi Q, Zhang H, et al. Detection of bird's nest of transmission lines from aerial images based on deep learning model[J]. International Journal of Innovative Computing, 2022, 18(6): 1755-1768.

薛昂, 姜恩宇, 张文涛, 等. 基于窗口自注意力网络与YOLOv5融合的输电线路通道异物检测[J]. 上海交通大学学报, 2025, 59(3): 413-423.

Xue A, Jiang E Y, Zhang W T, et al. Detection of foreign bodies in transmission line channels based on the fusion of swin transformer and YOLOv5[J]. Journal of Shanghai Jiaotong University, 2025, 59(3): 413-423.

Tang C L, Dong H Y, Huang Y R, et al. Foreign object detection for transmission lines based on swin Transformer V2 and YOLOX[J]. The Visual Computer, 2024, 40(5): 3003-3021.

Sun H B, Shen Q C, Ke H C, et al. Power transmission lines foreign object intrusion detection method for drone aerial images based on improved YOLOv8 network[J]. Drones, 2024, 8(8): 346.

王纪武, 罗海保, 鱼鹏飞, 等. 基于Faster R-CNN的多尺度高压塔鸟巢检测[J]. 北京交通大学学报, 2019, 43(5): 37-43.

Wang J W, Luo H B, Yu P F, et al. Bird's nest detection in multi-scale of high-voltage tower based on Faster R-CNN[J]. Journal of Beijing Jiaotong University, 2019, 43(5): 37-43.

李辉, 董燕, 刘祥, 等. 基于两阶段深度网络的输电线路异常目标检测方法[J]. 控制与决策, 2022, 37(7): 1873-1882.

Li H, Dong Y, Liu X, et al. Transmission line abnormal object detection method based on deep network of two-stage[J]. Control and Decision, 2022, 37(7): 1873-1882.

丁建, 黄陆明, 朱迪锋, 等. 面向高空塔架鸟巢检测的双尺度YOLOv3网络学习[J]. 西安理工大学学报, 2021, 37(2): 253-260.

Ding J, Huang L M, Zhu D F, et al. Learning to detect high altitude tower nest using dual-scale YOLOv3 network[J]. Journal of Xi'an University of Technology, 2021, 37(2): 253-260.

杨剑锋, 秦钟, 庞小龙, 等. 基于深度学习网络的输电线路异物入侵监测和识别方法[J]. 电力系统保护与控制, 2021, 49(4): 37-44.

Yang J F, Qin Z, Pang X L, et al. Foreign body intrusion monitoring and recognition method based on dense-YOLOv3 deep learning network[J]. Power System Protection and Control, 2021, 49(4): 37-44.

张焕坤, 李军毅, 张斌. 基于改进型YOLO v3的绝缘子异物检测方法[J]. 中国电力, 2020, 53(2): 49-55.

Zhang H K, Li J Y, Zhang B. Foreign object detection on insulators based on improved YOLO v3[J]. Electric Power, 2020, 53(2): 49-55.

刘国文, 张彩霞, 李斌, 等. 基于改进RetinaNet模型的接触网鸟巢检测[J]. 数据采集与处理, 2020, 35(3): 563-571.

Liu G W, Zhang C X, Li B, et al. Detection of Birds'nest in catenary based on improved RetinaNet model[J]. Journal of Data Acquisition & Processing, 2020, 35(3): 563-571.

Yu C H, Liu Y K, Zhang W R, et al. Foreign objects identification of transmission line based on improved YOLOv7[J]. IEEE Access, 2023, 11: 51997-52008.

魏业文, 李梅, 解园琳, 等. 基于改进Faster-RCNN的输电线路巡检图像检测[J]. 电力工程技术, 2022, 41(2): 171-178.

Wei Y W, Li M, Xie Y L, et al. Transmission line inspection image detection based on improved Faster-RCNN[J]. Jiangsu Electrical Engineering, 2022, 41(2): 171-178.

张鋆, 王继业, 宋睿, 等. 基于边缘智能的输电线路异常目标高效检测方法研究[J]. 电网技术, 2022, 46(5): 1652-1661.

Zhang J, Wang J Y, Song R, et al. Research on efficient detection technology of transmission line abnormal target based on edge intelligence[J]. Power System Technology, 2022, 46(5): 1652-1661.

Li H, Liu L Z, Du J, et al. An improved YOLOv3 for foreign objects detection of transmission lines[J]. IEEE Access, 2022, 10: 45620-45628.

Qiu Z B, Zhu X, Liao C B, et al. A lightweight YOLOv4-EDAM model for accurate and real-time detection of foreign objects suspended on power lines[J]. IEEE Transactions on Power Delivery, 2023, 38(2): 1329-1340.

Li H, Jiang F, Guo F, et al. A real-time detection method of safety hazards in transmission lines based on YOLOv5s[C]//Proceedings of SPIE Conference on International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022). Bellingham: SPIE Press, 2022: 283.

Li H, Dong Y Q, Liu Y X, et al. Design and implementation of UAVs for bird's nest inspection on transmission lines based on deep learning[J]. Drones, 2022, 6(9): 252.

Zhang H, Zhou X J, Shi Y K, et al. Object detection algorithm of transmission lines based on improved YOLOv5 framework[J]. Journal of Sensors, 2024(9): 1-14.

王杨杨, 曹晖, 莫文昊. 基于深度学习的改进型YOLOv4输电线路鸟巢检测与识别[J]. 智慧电力, 2023, 51(1): 101-107.

Wang Y Y, Cao H, Mo W H. Bird's nest detection and identification on improved YOLOv4 transmission line based on deep learning[J]. Smart Power, 2023, 51(1): 101-107.

姚云, 胡振虓, 邓涛, 等. 基于自适应池化注意力Transformer的唇语识别方法[J]. 智能科学与技术学报, 2025, 7(2): 211-220.

Yao Y, Hu Z X, Deng T, et al. A lip reading method based on adaptive pooling attention Transformer[J]. Chinese Journal of Intelligent Science and Technology, 2025, 7(2): 211-220.

杨澳男, 莫红, 赵世礼, 等. HCANet: 基于分层Transformer架构的微表情识别模型[J]. 智能科学与技术学报, 2025, 7(2): 277-286.

Yang A N, Mo H, Zhao S L, et al. HCANet: a micro expression recognition model based on hierarchical Transformer architecture[J]. Chinese Journal of Intelligent Science and Technology, 2025, 7(2): 277-286.

张志成, 王静, 张阳, 等. OrthoGPT: 面向精准诊疗的多模态骨科大模型[J]. 智能科学与技术学报, 2024, 6(3): 338-346.

Zhang Z C, Wang J, Zhang Y, et al. OrthoGPT: multimodal generative pre-trained transformer models for precise diagnosis and treatment of orthopedics[J]. Chinese Journal of Intelligent Science and Technology, 2024, 6(3): 338-346.

丛晓峰, 桂杰, 章军. 基于视觉Transformer的多损失融合水下图像增强网络[J]. 智能科学与技术学报, 2022, 4(4): 522-532.

Cong X F, Gui J, Zhang J. Underwater image enhancement network based on visual Transformer with multiple loss functions fusion[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 522-532.

Xu W F, Zhong X H, Luo M, et al. End-to-end insulator string defect detection in a complex background based on a deep learning model[J]. Frontiers in Energy Research, 2022, 10: 928162.

Cheng Y, Liu D M. An image-based deep learning approach with improved DETR for power line insulator defect detection[J]. Journal of Sensors, 2022(9): 1-22.

Li T Y, Zhu C S, Wang Y X, et al. LMFC-DETR: a lightweight model for real-time detection of suspended foreign objects on power lines[J]. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 2539319.

王震洲, 孙冬冬, 王建超, 等. 改进RT-DETR的输电线路异物检测算法研究[J]. 计算机工程与应用, 2026, 62(2): 116-125.

Wang Z Z, Sun D D, Wang J C, et al. Research on improved RT-DETR algorithm for foreign object detection in transmission lines[J]. Computer Engineering and Applications, 2026, 62(2): 116-125.

徐程, 郑春厚, 胡祥, 等. 基于TLI-DETR的输电线路巡检图像小目标检测方法[J]. 智慧电力, 2025, 53(9): 117-125.

Xu C, Zheng C H, Hu X, et al. A small object detection method for transmission line inspection images based on TLI-DETR[J]. Smart Power, 2025, 53(9): 117-125.

闫玮丹, 齐冬莲, 闫云凤, 等. 面向电力领域的知识图谱与大模型融合关键技术及其典型应用[J]. 高电压技术, 2025, 51(4): 1747-1762.

Yan W D, Qi D L, Yan Y F, et al. Key technologies and typical applications of knowledge graph and large language model fusion in the power sector[J]. High Voltage Engineering, 2025, 51(4): 1747-1762.

蒲天骄, 赵琦, 王新迎. 电力人工智能技术研究框架、应用现状及展望[J]. 电网技术, 2025, 49(5): 1751-1770.

Pu T J, Zhao Q, Wang X Y. Technology framework, application status and prospects on electric power artificial intelligence[J]. Power System Technology, 2025, 49(5): 1751-1770.

赵振兵, 冯烁, 席悦, 等. 大模型时代: 电力视觉技术新起点[J]. 高电压技术, 2024, 50(5): 1813-1825.

Zhao Z B, Feng S, Xi Y, et al. The era of large models: a new starting point for electric power vision technology[J]. High Voltage Engineering, 2024, 50(5): 1813-1825.

Guo X L, Bao Y F, Jiang H, et al. RDBL-net: detection of foreign objects on transmission lines based on positional encoding multiscale feature fusion[J]. International Journal of Sensor Networks, 2025, 47(2): 61-71.

Zhong Y Y, Luo P X, Yan Y F, et al. PowerGPT: a multimodal foundation model for power inspection[J].Applied Soft Computing, 2026, 186: 456-462.

Wang J H, Li M X, Luo H C, et al. Power-Llava: large language and vision assistant for power transmission line inspection[C]//Proceedings of the 2024 IEEE International Conference on Image Processing (ICIP). Piscataway: IEEE Press, 2024: 963-969.

曹现刚, 刘航, 刘家辉, 等. 面向原煤分选场景的多模态融合异物开集检测方法[J]. 煤炭科学技术, 2026, 54(1): 464-474.

Cao X G, Liu H, Liu J H, et al. Multimodal fusion method for foreign object open-set detection in raw coal sorting scenario[J]. Coal Science and Technology, 2026, 54(1): 464-474.

张辉, 杜瑞, 钟杭, 等. 电力设施多模态精细化机器人巡检关键技术及应用[J]. 自动化学报, 2025, 51(1): 20-42.

Zhang H, Du R, Zhong H, et al. The key technology and application of multi-modal fine robot inspection for power facilities[J]. Acta Automatica Sinica, 2025, 51(1): 20-42.

王涓, 赵大伟, 朱琪, 等. 大模型驱动的云边协同多智能体具身学习[J]. 自动化学报, 2026, doi: 10.16383/j.aas.c260096.

Wang J, Zhao D W, Zhu Q, et al. Cloud-edge collaborative multi-agent embodied learning driven by large models[J]. Acta Automatica Sinica, 2026, doi: 10.16383/j.aas.c260096.

吴杏平, 周飞, 吴春鹏, 等. 电力人工智能算力网络原型构建与调度验证[J]. 电力系统自动化, 2026, doi: cnki.net/urlid/32.1180.TP.20260513.2151.008.

Wu X P, Zhou F, Wu C P, et al. Prototype construction and scheduling verification of computing network for electric power artificial intelligence[J]. Automation of Electric Power Systems, 2026, doi: cnki.net/urlid/32.1180.TP.20260513.2151.008.

Waga A, Benhlima S, Bekri A, et al. A survey on autonomous navigation for mobile robots: from traditional techniques to deep learning and large language models[J]. Journal of King Saud University Computer and Information Sciences, 2025, 37(7): 198.

李柏, 郝金第, 孙跃硕, 等. 平行智能范式视角下的视觉-语言-动作模型发展现状与展望[J]. 智能科学与技术学报, 2025, 7(3): 290-303.

Li B, Hao J D, Sun Y S, et al. Vision-language-action models under parallel intelligence paradigm: the state of the art and future perspectives[J]. Chinese Journal of Intelligent Science and Technology, 2025, 7(3): 290-303.

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