新型YOLOv3-Tiny在绝缘子故障检测中的应用OA
Application of New YOLOv3-Tiny in Insulator Fault Detection
由于输电线路绝缘子故障直接影响电网稳定运行,为准确识别绝缘子故障,提出一种基于改进YOLOv3(You Only Look Once 3)-Tiny的绝缘子故障诊断方法.首先,在特征提取网络中集成通道-空间注意力机制,强化关键特征捕获能力.然后,设计跨阶段部分连接-感受野模块,以增强小目标的检测能力,同时减少计算量.最后,采用一种新颖的损失函数优化定位精度.实验表明,改进后的YOLOv3-Tiny算法在检测绝缘子故障方面表现出高达97.4％的平均准确率,超越原始YOLOv3-Tiny算法.
Insulator faults in transmission lines directly threaten power grid stability.To accurately identify insulator faults,an insulator fault diagnosis method based on an improved YOLOv3(You Only Look Once v3)-Tiny framework is proposed.Firstly,a channel-spatial attention mechanism is integrated into the feature extraction network to enhance critical feature capture capabilities.Subsequently,a CSP-RFB(Cross Stage Partial-Receptive Field Block)module is designed to improve small-target detection performance while reducing computational complexity.Finally,a novel loss function is adopted to optimize localization accuracy.Experimental results demonstrate that the enhanced YOLOv3-Tiny algorithm achieves up to 97.4％MAP(Mean Average Precision)in insulator fault detection,significantly outperforming the original YOLOv3-Tiny model.
王炳北;郑辉;孙德罡
大庆油田有限责任公司第三采油厂工艺研究所,黑龙江大庆 163113大庆油田有限责任公司第一采油厂数字化运维中心,黑龙江大庆 163000大庆石化公司设备维修中心化工区仪表一车间,黑龙江大庆 163714
信息技术与安全科学
深度学习缺陷检测绝缘子故障诊断
deep learningdefect detectioninsulator fault diagnosis
《吉林大学学报（信息科学版）》 2026 (1)
61-70,10
海南省自然科学基金资助项目(623MS071)
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