TL-YOLO: Foreign-Object Detection on Power Transmission Line Based on Improved Yolov8

被引:6
|
作者
Shao, Yeqin [1 ]
Zhang, Ruowei [2 ]
Lv, Chang [1 ]
Luo, Zexing [1 ]
Che, Meiqin [1 ]
机构
[1] Nantong Univ, Sch Transportat & Civil Engn, Nantong 226019, Peoples R China
[2] Nantong Univ, Sch Elect Engn, Nantong 226004, Peoples R China
基金
中国国家自然科学基金;
关键词
power transmission line; foreign-object detection; Yolov8; attention mechanism; feature fusion;
D O I
10.3390/electronics13081543
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Foreign objects on power transmission lines carry a significant risk of triggering large-scale power interruptions which may have serious consequences for daily life if they are not detected and handled in time. To accurately detect foreign objects on power transmission lines, this paper proposes a TL-Yolo method based on the Yolov8 framework. Firstly, we design a full-dimensional dynamic convolution (ODConv) module as a backbone network to enhance the feature extraction capability, thus retaining richer semantic content and important visual features. Secondly, we present a feature fusion framework combining a weighted bidirectional feature pyramid network (BiFPN) and multiscale attention (MSA) module to mitigate the degradation effect of multiscale feature representation in the fusion process, and efficiently capture the high-level feature information and the core visual elements. Thirdly, we utilize a lightweight GSConv cross-stage partial network (GSCSP) to facilitate efficient cross-level feature fusion, significantly reducing the complexity and computation of the model. Finally, we employ the adaptive training sample selection (ATSS) strategy to balance the positive and negative samples, and dynamically adjust the selection process of the training samples according to the current state and performance of the model, thus effectively reducing the object misdetection and omission. The experimental results show that the average detection accuracy of the TL-Yolo method reaches 91.30%, which is 4.20% higher than that of the Yolov8 method. Meanwhile, the precision and recall metrics of our method are 4.64% and 3.53% higher than those of Yolov8. The visualization results also show the superior detection performance of the TL-Yolo algorithm in real scenes. Compared with the state-of-the-art methods, our method achieves higher accuracy and speed in the detection of foreign objects on power transmission lines.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Improved YOLOv8n for Foreign-Object Detection in Power Transmission Lines
    Wang, Hanjun
    Luo, Shiyu
    Wang, Qun
    IEEE ACCESS, 2024, 12 : 121433 - 121440
  • [2] Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
    Wang, Zhenyue
    Yuan, Guowu
    Zhou, Hao
    Ma, Yi
    Ma, Yutang
    Iotti, Eleonora
    Bonnici, Vincenzo
    Bertini, Flavio
    APPLIED SCIENCES-BASEL, 2023, 13 (23):
  • [3] A Raisin Foreign Object Target Detection Method Based on Improved YOLOv8
    Ning, Meng
    Ma, Hongrui
    Wang, Yuqian
    Cai, Liyang
    Chen, Yiliang
    APPLIED SCIENCES-BASEL, 2024, 14 (16):
  • [4] Cross-YOLO: an object detection algorithm for UAV based on improved YOLOv8 model
    Ying Dong
    Jiahao Guo
    Fucheng Xu
    Signal, Image and Video Processing, 2025, 19 (6)
  • [5] Power Transmission Lines Foreign Object Intrusion Detection Method for Drone Aerial Images Based on Improved YOLOv8 Network
    Sun, Hongbin
    Shen, Qiuchen
    Ke, Hongchang
    Duan, Zhenyu
    Tang, Xi
    DRONES, 2024, 8 (08)
  • [6] PF-YOLO: An Improved YOLOv8 for Small Object Detection in Fisheye Images
    Cheng Zhang
    Cheng Xu
    Hongzhe Liu
    Journal of Beijing Institute of Technology, 2025, 34 (01) : 57 - 70
  • [7] MSFE-YOLO: An Improved YOLOv8 Network for Object Detection on Drone View
    Qi, Shuaihui
    Song, Xiaofeng
    Shang, Tongfei
    Hu, Xiaochang
    Han, Kun
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2024, 21
  • [8] YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
    Wu, Tianyong
    Dong, Youkou
    APPLIED SCIENCES-BASEL, 2023, 13 (24):
  • [9] Improved YOLOv8 for Small Object Detection
    Xue, Huafeng
    Chen, Jilin
    Tang, Ruichun
    2024 5TH INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKS AND INTERNET OF THINGS, CNIOT 2024, 2024, : 266 - 272
  • [10] Road Object Detection Algorithm Based on Improved YOLOv8
    Peng, Jun
    Li, Chenxi
    Jiang, Aiping
    Mou, Biao
    Lu, Yiyi
    Chen, Wei
    2024 IEEE 19TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, ICIEA 2024, 2024,