基于改进YOLOv5的绝缘子污损检测方法OA
Insulator Fouling Detection Method Based on Improved YOLOv5
基于机器视觉对绝缘子污损状况进行检测是提高电力系统巡检自动化程度、提升巡检效率的重要方法.针对目前基于深度神经网络的绝缘子污损的检测速度慢、网络复杂度高、难以实现精准检测等问题,提出一种基于改进YOLOv5 的绝缘子污损检测方法.采用GhostNet网络轻量化设计YOLOv5 的主干网络,降低网络的复杂度,大幅提高模型的检测速度;在特征提取网络中引入CBAM注意力机制,增强模型的感知能力,提高模型的检测精度.试验结果表明,与YOLOv5 模型相比…查看全部>>
Machine vision based detection of insulator fouling damage is an important method to improve the automation of power sys-tem inspection and inspection efficiency.According to the problems of slow detection speed,high network complexity,and difficulty in achieving accurate detection of insulator fouling damage detection based on deep neural networks,an improved YOLOv5 based insu-lator fouling detection method is proposed.The GhostNet network is used to lightw…查看全部>>
张全辉;赵晋级
国网淮南市潘集区供电公司,安徽 淮南 232000国网淮南市潘集区供电公司,安徽 淮南 232000
动力与电气工程
绝缘子污损检测YOLOv5CBAM注意力机制
insulatorfouling detectionYOLOv5CBAM attention mechanism
《东北电力技术》 2024 (11)
58-62,5
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