YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines

Liu, Chunyang; Ma, Lin; Sui, Xin; Guo, Nan; Yang, Fang; Yang, Xiaokang; Huang, Yan; Wang, Xiao

doi:10.3390/electronics13010123

Open AccessArticle

YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines

by

Chunyang Liu

^1,2

,

Lin Ma

^1,*,

Xin Sui

^1,3,

Nan Guo

^1,3,

Fang Yang

^1,3,

Xiaokang Yang

¹,

Yan Huang

¹ and

Xiao Wang

¹

School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China

²

Longmen Laboratory, Luoyang 471003, China

³

Key Laboratory of Mechanical Design and Transmission System of Henan Province, Luoyang 471003, China

^*

Author to whom correspondence should be addressed.

Electronics 2024, 13(1), 123; https://doi.org/10.3390/electronics13010123

Submission received: 28 November 2023 / Revised: 25 December 2023 / Accepted: 26 December 2023 / Published: 28 December 2023

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Abstract

Detecting component defects and attaching tiny-scaled foreign objects to the overhead transmission lines are critical to the national grid’s safe operation and power distribution. This urgent task, however, faces challenges, such as the complex working environment and the considerable amount of workforce investment, for which we propose a deep-learning-aided object detection approach, YOLO-CSM, to address the issue. Combined with two attention mechanisms (Swin transformer and CBAM) and an extra detection layer, the proposed model can effectively capture global information and key visual features and promote its ability to identify tiny-scaled defects and distant objects in the visual fields. In order to validate this model, this work consolidates a dataset composed of public images and our field-taken picture samples. The experiment verifies YOLO-CSM as a suitable solution for small and distant object detection that outperforms several well-used algorithms, featuring a 16.3% faster detection speed than YOLOv5 and a 3.3% better detection accuracy than YOLOv7. Finally, this work conducts an interpretability experiment to reveal the similarity between YOLO-CSM’s attention patterns and that of humans, aiming to explain YOLO-CSM’s advantages in detecting small objects and minor defects in the working environments of power transmission lines.

Keywords: power transmission line inspection; YOLOv7; object and defect detection; swin transformer; CBAM

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MDPI and ACS Style

Liu, C.; Ma, L.; Sui, X.; Guo, N.; Yang, F.; Yang, X.; Huang, Y.; Wang, X. YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines. Electronics 2024, 13, 123. https://doi.org/10.3390/electronics13010123

AMA Style

Liu C, Ma L, Sui X, Guo N, Yang F, Yang X, Huang Y, Wang X. YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines. Electronics. 2024; 13(1):123. https://doi.org/10.3390/electronics13010123

Chicago/Turabian Style

Liu, Chunyang, Lin Ma, Xin Sui, Nan Guo, Fang Yang, Xiaokang Yang, Yan Huang, and Xiao Wang. 2024. "YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines" Electronics 13, no. 1: 123. https://doi.org/10.3390/electronics13010123

APA Style

Liu, C., Ma, L., Sui, X., Guo, N., Yang, F., Yang, X., Huang, Y., & Wang, X. (2024). YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines. Electronics, 13(1), 123. https://doi.org/10.3390/electronics13010123

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YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines

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