Research on Insulator Defect Detection Based on Improved YOLOv7 and Multi-UAV Cooperative System
Source abstract
Abstract
Insulator self-blasts, cracked insulators, and bird nests often lead to large-scale power outages and safety accidents, while the detection system based on a single UAV and YOLOv7 is difficult to meet the speed and accuracy requirements in actual detection. Therefore, a novel insulator defect detection method based on improved YOLOv7 and a multi-UAV collaborative system is proposed innovatively. Firstly, a complete insulator defects dataset is constructed, and the introduction of insulator self-blasts, cracked insulators, and bird nest images avoids the problem of low reliability for single defect detection. Secondly, a multi-UAV collaborative platform is proposed, which improves the search scope and efficiency. Most critically, an improved YOLOv7-C3C2-GAM is proposed. The introduction of the C3C2 module and the CNeB2 structure improves the efficiency and accuracy of feature extraction, and the introduction of a global attention mechanism (GAM) improved the feature extraction ability to extract key information about small targets or occluded targets and feature in the region of interest. Compared with YOLOv7, the accuracies of YOLOv7-C3C2 and YOLOv7-C3C2-GAM are improved by 1.3% and 0.5%, respectively, the speed of YOLOv7-C3C2 is improved by 0.1 ms, and the lightweight sizes are reduced by 8.2 Mb and 8.1 Mb, respectively. Therefore, the proposed method provides theoretical and technical support for power equipment defect detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Industrial Vision Systems and Defect Detection · Image Enhancement Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Yuxi Power Supply Bureau, Yunnan Power Grid Corporation, Yuxi 653100, China
The Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming 650500, China
Electric Power Research Institute, Yunnan Power Grid Corporation, Kunming 650214, China
School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China
Funding
Science and technology project of China Southern Power Grid Co., Ltd. · Yunnan Normal University
References (40)
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