Affiliation:
1. School of Electrical and Information Engineering, Suzhou University of Science and Technology, Suzhou 215101, China
2. School of Electrical Engineering, Nantong University, Nantong 226019, China
3. School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215028, China
Abstract
Object detection of overhead transmission lines is a solution for promoting inspection efficiency for power companies. However, aerial images contain many complex backgrounds and small objects, and traditional algorithms are incompetent in the identification of details of power transmission lines accurately. To address this problem, this paper develops an object detection method based on optimized You Only Look Once v5-small (YOLOv5s). This method is designed to be engineering-friendly, with the objective of maximal detection accuracy and computation simplicity. Firstly, to improve the detecting accuracy of small objects, a larger scale detection layer and jump connections are added to the network. Secondly, a self-attention mechanism is adopted to merge the feature relationships between spatial and channel dimensions, which could suppress the interference of complex backgrounds and boost the salience of objects. In addition, a small object enhanced Complete Intersection over Union (CIoU) is put forward as the loss function of the bounding box regression. This loss function could increase the derived loss for small objects automatically, thereby improving the detection of small objects. Furthermore, based on the scaling factors of batch-normalization layers, a pruning method is adopted to reduce the parameters and achieve a lightweight method. Finally, case studies are fulfilled by comparing the proposed method with classic YOLOv5s, which demonstrate that the detection accuracy is increased by 4%, the model size is reduced by 58%, and the detection speed is raised by 3.3%.
Funder
Key Program of National Natural Science Foundation of China
the Key Research and Development Plan of Jiangsu Province
the Natural Science Foundation of the Jiangsu Higher Education Institutions of China
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference30 articles.
1. Gu, J., Hu, J., and Jiang, L. (2022, January 23–25). Object detection of overhead transmission lines based on improved YOLOv5s. Proceedings of the 12th International Conference on Power and Energy Systems (ICPES), Guangzhou, China.
2. A review on state-of-the-art power line inspection techniques;Yang;IEEE Trans. Instrum. Meas.,2020
3. Intelligent monitoring and Inspection of power line components powered by UAVs and deep learning;Nguyen;IEEE Power Energy Technol. Syst. J.,2019
4. Multi-stage deep learning networks for automated assessment of electricity transmission infrastructure using fly-by images;Manninen;Electr. Power Syst. Res.,2022
5. Fault diagnosis of power transmission lines using a UAV-mounted smart inspection System;Kim;IEEE Access,2020
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献