Advancements and Prospects in Wire Identification: A Comprehensive Review of the ED-YOLO Algorithm

Kai Wei

doi:10.47297/taposatWSP2633-456929.20250602

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Advancements and Prospects in Wire Identification: A Comprehensive Review of the ED-YOLO Algorithm

Volume 6, Issue 2

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Advancements and Prospects in Wire Identification: A Comprehensive Review of the ED-YOLO Algorithm
Advancements and Prospects in Wire Identification: A Comprehensive Review of the ED-YOLO Algorithm
School of Mechanical Engineering, Chongqing University of Technology, Chongqing, 400054, China
Vol. 6, Issue 2, Pages: 153-156(2025)
Published：25 February 2025
DOI：10.47297/taposatWSP2633-456929.20250602
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Wei Kai.Advancements and Prospects in Wire Identification: A Comprehensive Review of the ED-YOLO Algorithm[J].Theory and Practice of Science and Technology,2025,06(02):153-156.
DOI：

Wei Kai.Advancements and Prospects in Wire Identification: A Comprehensive Review of the ED-YOLO Algorithm[J].Theory and Practice of Science and Technology,2025,06(02):153-156. DOI： 10.47297/taposatWSP2633-456929.20250602.

摘要

Abstract

In modern computer recognition

the YOLO algorithm is a widely used approach due to its real-time performance

simplicity in structure

and low memory consumption

outperforming R-CNN. The ED-YOLO algorithm is an improved version of YOLOv4

offering significant performance enhancements. This paper provides a concise introduction to ED-YOLO and discusses its importance in powerline recognition. Subsequently

the paper delves into the key components and technical details of the ED-YOLO algorithm

focusing on the addition of channel attention mechanisms and the incorporation of depthwise separable convolutions into PANet. Challenges and solutions for its application are analyzed

alongside a summary of recent advancements and future directions in powerline recognition. The aim of this paper is to offer researchers a comprehensive overview of unmanned aerial vehicle (UAV)-based powerline recognition using ED-YOLO

promoting further research and application in this field.

关键词

Keywords

references

Sui, Y., Ning, P. F., Niu, P. J., et al. (2021). Overview of Research on Power Inspection Technology of Mounted Drones for Overhead Transmission Conductors. Power System Technology, 45(9): 3636-3648.

Bochkovskiy A, Wang C Y, Liao H Y. YOLOv4: Optimal speed and accuracy of object detection. arXiv: 2004, 10934, 2020.

Wang Q, Wu B, Zhu P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks. CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2020.

Peng, J. S., Sun, L. X., Wang, K., et al. (2023). ED-YOLO Obstacle-Avoidance Object Detection Algorithm for Power Inspection UAVs Based on Model Compression. Chinese Journal of Scientific Instrument, (10): 160 - 169.

Rezatofighi H, Tsoi N, Gwak Y, et al. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. Conference on Computer Vision and Pattern Recognition(CVPR). 2019: 658-666.

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