摘要
高压电缆运行在电缆隧道环境中,需要工作人员进行周期性巡视以发现异常故障.为了提高工作效率,及时发现不良工况,提出了一种基于广域极差的机器视觉跟踪成像异常故障检测方法.该方法能够利用广域极差在复杂背景和光照条件下极高对比度和稳定性的特性,快速准确地定位和跟踪电缆隧道内异常积水情况,并通过机器视觉构图技术,生成高质量的积水图像.通过仿真试验和现场试验验证了所提出方法的有效性.仿真试验结果表明,该方法能够在不同视角和距离下实现隧道内积水目标稳定跟踪和清晰成像,且具有较高的识别率和跟踪率.现场试验结果表明,该方法能够在实际变电所环境中自动完成电缆隧道内积水故障检测任务,且具有较高的准确率.
Abstract
High voltage cables operate in cable tunnel environments and require periodic inspections by personnel to detect abnormal faults.A machine vision tracking imaging anomaly fault detection method based on wide range range range is proposed to improve personnel work efficiency and detect adverse working conditions in a timely manner.This method can utilize the characteristics of extremely high contrast and stability in complex back-grounds and lighting conditions with wide area range,quickly and accurately locate and track abnormal water accu-mulation in cable tunnels,and generate high-quality lightning arrester images through machine vision composition technology.The effectiveness of the method has been verified through simulation experiments and on-site experi-ments.The results show that the method can achieve stable tracking and clear imaging of water accumulation tar-gets in tunnels under different perspectives and distances,and has high recognition and tracking rates.The on-site test results show that this method can automatically complete the task of detecting water accumulation faults in cable tunnels in actual substation environments,and has high accuracy.
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