An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8
Source abstract
Abstract
Aiming at the problem that insulator image backgrounds are complex and fault types are diverse, which makes it difficult for existing deep learning algorithms to achieve accurate insulator fault diagnosis, an insulator fault diagnosis method based on multi-mechanism optimization YOLOv8-DCP is proposed. Firstly, a feature extraction and fusion module, named CW-DRB, was designed. This module enhances the C2f structure of YOLOv8 by incorporating the dilation-wise residual module and the dilated re-param module. The introduction of this module improves YOLOv8’s capability for multi-scale feature extraction and multi-level feature fusion. Secondly, the CARAFE module, which is feature content-aware, was introduced to replace the up-sampling layer in YOLOv8n, thereby enhancing the model’s feature map reconstruction ability. Finally, an additional small-object detection layer was added to improve the detection accuracy of small defects. Simulation results indicate that YOLOv8-DCP achieves an accuracy of 97.7% and an mAP@0.5 of 93.9%. Compared to YOLOv5, YOLOv7, and YOLOv8n, the accuracy improved by 1.5%, 4.3%, and 4.8%, while the mAP@0.5 increased by 3.0%, 4.3%, and 3.1%. This results in a significant enhancement in the accuracy of insulator fault diagnosis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Fire Detection and Safety Systems · Image Enhancement Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Hubei Provincial Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan 430200, China
School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan 430200, China
Hubei Engineering Research Center of Industrial Detonator Intelligent Assembly, Wuhan Textile University, Wuhan 430200, China
Hunan Province Key Laboratory of Intelligent Live Working Technology and Equipment (ROBOT), State Grid Hunan Ultra-High Voltage Transmission Company, Changsha 420100, China
Funding
Hunan Electric Power Co., Ltd. Project
References (28)
Show source-provided references
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