基于Real-ESRGAN和改进YOLOv8n的输电线路绝缘子故障检测  

Fault detection for transmission line insulators based on Real-ESRGAN and improved YOLOv8n

作　　者：任一鸣 杜董生 邓祥帅 连贺 赵哲敏 REN Yiming;DU Dongsheng;DENG Xiangshuai;LIAN He;ZHAO Zhemin(School of Automation,Huaiyin Institute of Technology,Huai'an 223003,China)

机构地区：[1]淮阴工学院自动化学院,江苏淮安223003

出　　处：《综合智慧能源》2024年第7期29-39,共11页Integrated Intelligent Energy

基　　金：国家自然科学基金项目(61873107);江苏省“青蓝工程”中青年学术带头人培养计划项目。

摘　　要：为解决无人机在输电线路巡检时遇到的绝缘子故障难以检测的问题,提出一种绝缘子故障检测新方法。该方法结合了真实世界增强超分辨率生成对抗网络(Real-ESRGAN)和改进的YOLOv8n。首先,利用Real-ESRGAN对数据集进行超分辨率重构,优化数据集质量,有效减少复杂背景的干扰;然后利用高效视觉变压器框架替换YOLOv8的主干,加强模型的特征提取能力,同时使模型在推理阶段有更快的处理速度;再对YOLOv8的检测头进行轻量化处理,进一步加速模型推理。试验结果显示,该方法的均值平均精度达86.7%,证明了其在复杂背景下的卓越目标检测性能。通过分析热力图,展示了该算法与传统YOLOv8在关注区域上的差异,从而揭示了模型的内部工作机理。To solve the difficulties in insulator fault detection during drone-based transmission line inspections,an innovative method for insulator fault detection which combines Real Enhanced Super Resolution Generative Adversarial Network(Real-ESRGAN)and improved YOLOv8n is proposed.Firstly,Real-ESRGAN is adopted to perform super-resolution reconstruction on the dataset,in order to optimize the quality of the dataset and effectively reduce the interference of complex backgrounds.Then,an efficient visual transformer framework is used to replace the backbone of YOLOv8,enhancing the model's feature extraction ability and accelerating the image process at inference stage.Lightweight processing is carried out on detection heads of YOLOv8 to further accelerate the inference of the model.Experimental results show that the mean average precision of this detection method reaches 86.7%,demonstrating its excellent object detection performance in complex backgrounds.Finally,by analyzing heat maps,the differences between the results made by the proposed method and the traditional YOLOv8 in the focus area are displayed,revealing the internal working mechanism of the mode.

关 键 词：目标检测 输电线路 绝缘子 无人机 YOLOv8 超分辨重构 生成对抗网络 

分 类 号：TM721[电气工程—电力系统及自动化]