电子测量与仪器学报

贾晓芬,吴雪茹,赵佰亭.绝缘子自爆缺陷的轻量化检测网络 DE-YOLO[J].电子测量与仪器学报,2023,37(5):28-35

绝缘子自爆缺陷的轻量化检测网络 DE-YOLO

Lightweight detection network for insulator self-detonation defect DE-YOLO

DOI：

英文关键词:insulator self-exploding defect deep learning attention mechanism depth-separable convolution

基金项目:国家自然科学基金面上项目( 52174141)、安徽省自然科学基金面上项目( 2108085ME158)、安徽高校协同创新项目(GXXT-2020- 54)、安徽省重点研究与开发计划项目(202104a07020005)资助

作者	单位
贾晓芬	1. 安徽理工大学电气与信息工程学院，2. 安徽理工大学省部共建深部煤矿采动响应与灾害防控国家重点实验室
吴雪茹	1. 安徽理工大学电气与信息工程学院
赵佰亭	1. 安徽理工大学电气与信息工程学院

Author	Institution
Jia Xiaofen	1. China Institute of Electrical and Information Engineering, Anhui University of Science and Technology，
Wu Xueru	1. China Institute of Electrical and Information Engineering, Anhui University of Science and Technology
Zhao Baiting	1. China Institute of Electrical and Information Engineering, Anhui University of Science and Technology

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中文摘要:

为了实现输电线路的高精度、高速度巡检,设计了一种适用于移动终端设备的轻量化目标检测网络 DE-YOLO。首先融合深度可分离卷积、逐点卷积和 ECA 注意力机制提出了特征提取模块 NewC3,它负责显著降低网络参数、同时强化网络提取绝缘子有效信息的能力。再借助通道数成倍增长策略和通道注意力机制 SE 设计了轻量化模块 DC-SE,它用于削弱复杂背景对绝缘子故障的干扰、互补提取绝缘子细微特征,进而增强浅层网络对目标特征信息的提取能力。实验表明,DE-YOLO 网络在自制绝缘子数据集上的 GFLOPs 降低 45%,运行参数降低 42%,自爆缺陷检测精度高达 93. 2%。 NewC3 和 DC-SE 能保证 DE-YOLO 的轻量化,同时满足绝缘子自爆缺陷实时检测的要求。

英文摘要:

In order to meet the requirements of high detection accuracy, fast inspection speed and easy to be embedded in mobile devices, a lightweight object detection structure DE-YOLO is designed for mobile terminal devices. Firstly, combining depth-separable convolution, point-by-point convolution and ECA attention mechanism, the feature extraction module NewC3 is proposed, which is responsible for significantly reducing network parameters and strengthening the ability of network to extract effective insulator information. Then, the lightweight module DC-SE is designed with the help of channel number multiplication strategy and channel attention mechanism SE. It is used to weaken the interference of complex background to insulator fault, extract the subtle features of insulator complementary, and then enhance the extraction ability of target feature information of shallow network. Experiments show that the GFLOPs of DE-YOLO network on the expanded Homemade insulator data dataset are reduced by 45%, the running parameters are reduced by 42%, and the detection accuracy of self-exploding defects is up to 93. 2%. NewC3 and DC-SE can ensure the lightweight of DE-YOLO and meet the requirements of real-time detection of self-exploding insulator defects.

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