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Device Target Checking for Power Patrol Robot Based on Objectness Estimation

机译:基于目标估计的动力巡逻机器人设备目标检查

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In order to free people from daily patrol tasks in power substations, patrol robots are designed to check power equipment status and read various electric meters. According to the requirements of patrol tasks, a hierarchical, coarse-to-fine, fast device detection and recognition method is proposed. The coarse detection is based on objectness, using 8 × 8 Binarized Normed Gradients (BING) feature to generate proposals and filtering them using Support Vector Machine(SVM) trained by Local Binary Pattern(LBP) feature combining with histogram matching. The fine detection utilizes the color and contour feature of the basic elements of a certain device to obtain the status information or meter data. In this paper, instances of the device status checking and the pointer meter data reading are developed. The experiments validate the effectiveness, accuracy and real-time of this method.
机译:为了使人们摆脱变电站的日常巡逻任务,巡逻机器人被设计用来检查电力设备的状态并读取各种电表。根据巡逻任务的要求,提出了一种分级的,从粗到精,快速的设备检测与识别方法。粗略检测基于客观性,使用8×8二进制归一化梯度(BING)功能生成建议,并使用由本地二进制模式(LBP)功能训练的支持向量机(SVM)与直方图匹配结合对建议进行过滤。精细检测利用特定设备基本元素的颜色和轮廓特征来获取状态信息或仪表数据。在本文中,开发了设备状态检查和指针仪表数据读取的实例。实验验证了该方法的有效性,准确性和实时性。

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