Research on Object Detection of Overhead Transmission Lines Based on Optimized YOLOv5s

Juping Gu (), Junjie Hu, Ling Jiang, Zixu Wang, Xinsong Zhang, Yiming Xu, Jianhong Zhu and Lurui Fang
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Juping Gu: School of Electrical and Information Engineering, Suzhou University of Science and Technology, Suzhou 215101, China
Junjie Hu: School of Electrical Engineering, Nantong University, Nantong 226019, China
Ling Jiang: School of Electrical Engineering, Nantong University, Nantong 226019, China
Zixu Wang: School of Electrical Engineering, Nantong University, Nantong 226019, China
Xinsong Zhang: School of Electrical Engineering, Nantong University, Nantong 226019, China
Yiming Xu: School of Electrical Engineering, Nantong University, Nantong 226019, China
Jianhong Zhu: School of Electrical Engineering, Nantong University, Nantong 226019, China
Lurui Fang: School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215028, China

Energies, 2023, vol. 16, issue 6, 1-18

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%.

Keywords: overhead transmission line; object detection; larger scale detection layer; self-attention; bounding box regression; lightweight (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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