Open Access

ARTICLE

A Novel Foreign Object Detection Method in Transmission Lines Based on Improved YOLOv8n

Yakui Liu^1,2,3,*, Xing Jiang¹, Ruikang Xu¹, Yihao Cui¹, Chenhui Yu¹, Jingqi Yang¹, Jishuai Zhou¹

1 School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, 266520, China
2 State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an, 710049, China
3 Key Lab of Industrial Fluid Energy Conservation and Pollution Control, Qingdao University of Technology, Ministry of Education, Qingdao, 266520, China

* Corresponding Author: Yakui Liu. Email:

Computers, Materials & Continua 2024, 79(1), 1263-1279. https://doi.org/10.32604/cmc.2024.048864

Received 20 December 2023; Accepted 11 March 2024; Issue published 25 April 2024

Abstract

The rapid pace of urban development has resulted in the widespread presence of construction equipment and increasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safe operation of the power grid. Machine vision technology, particularly object recognition technology, has been widely employed to identify foreign objects in transmission line images. Despite its wide application, the technique faces limitations due to the complex environmental background and other auxiliary factors. To address these challenges, this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replaced with a spatial-depth convolution (SPD-Conv) module, aiming to improve the algorithm’s efficacy in recognizing low-resolution and small-size objects. The algorithm's feature extraction network is improved by using a Large Selective Kernel (LSK) attention mechanism, which enhances the ability to extract relevant features. Additionally, the SIoU Loss function is used instead of the Complete Intersection over Union (CIoU) Loss to facilitate faster convergence of the algorithm. Through experimental verification, the improved YOLOv8n model achieves a detection accuracy of 88.8% on the test set. The recognition accuracy of cranes is improved by 2.9%, which is a significant enhancement compared to the unimproved algorithm. This improvement effectively enhances the accuracy of recognizing foreign objects on transmission lines and proves the effectiveness of the new algorithm.

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Keywords

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