Infrared Image Object Detection Algorithm for Substation Equipment Based on Improved YOLOv8

Siyu Xiang, Zhengwei Chang, Xueyuan Liu, Lei Luo, Yang Mao, Xiying Du, Bing Li () and Zhenbing Zhao
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Siyu Xiang: State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Zhengwei Chang: State Grid Sichuan Electric Power Company, Chengdu 610095, China
Xueyuan Liu: State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Lei Luo: State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Yang Mao: State Grid SiChuan GuangYuan Electric Power Company, Guangyuan 628033, China
Xiying Du: Department of Automation, North China Electric Power University, Baoding 071003, China
Bing Li: Department of Automation, North China Electric Power University, Baoding 071003, China
Zhenbing Zhao: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China

Energies, 2024, vol. 17, issue 17, 1-15

Abstract: Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves mAP@0.5 and mAP@0.5:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment.

Keywords: substation equipment; object detection; YOLOv8n; DCNC2f; multi-scale convolutional attention module (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: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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