Insulator defect detection method based on improved YOLO attention enhancement and related equipment
By improving the YOLOv8 attention-enhanced insulator defect detection method, and utilizing feature extraction and multi-scale fusion techniques, the problem of high-precision identification of insulator defects in transmission lines was solved, thereby improving detection accuracy and safety.
CN122415431APending Publication Date: 2026-07-17FIBRLINK NETWORKS
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FIBRLINK NETWORKS
- Filing Date
- 2026-03-11
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing technologies cannot effectively solve the problem of high-precision identification and real-time inspection of insulator defects in transmission lines, which threatens the safety and stability of the power grid.
Method used
An improved YOLOv8 attention-enhanced insulator defect detection method is adopted. By acquiring insulator images and extracting features using a pre-trained defect detection model, multi-scale fusion processing is performed by combining spatial and channel attention-enhanced features to improve detection accuracy.
Benefits of technology
It significantly improves the accuracy of insulator defect identification, reduces the probability of missed and false detections, and provides a guarantee for the safe operation of transmission lines.
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Figure CN122415431A_ABST
Abstract
本公开提供一种基于改进YOLO注意力增强的绝缘子缺陷检测方法及相关设备。所述方法包括：获取输电线路的绝缘子图像，并将所述绝缘子图像输入至预先训练过的缺陷检测模型；利用所述缺陷检测模型，对所述绝缘子图像进行特征提取处理得到空间注意力增强特征和通道注意力增强特征；根据所述空间注意力增强特征和所述通道注意力增强特征确定注意力协调特征；对所述注意力协调特征进行多尺度融合处理得到目标多尺度融合特征；利用所述缺陷检测模型，根据所述目标多尺度融合特征确定绝缘子缺陷检测结果。
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