变电站指针式仪表示数识别方法研究

Research on Reading Recognition Method of Substation Pointer-type Meters

为对处于复杂环境中的变电站指针式仪表进行示数识别,提出一种基于深度学习的指针式仪表示数识别方法。首先使用目标检测算法YOLOv3检测图片中仪表和仪表刻度值的位置,并使用基于LeNet-5网络的字符识别算法识别刻度数值;然后使用语义分割算法DeepLabv3+分割出仪表指针区域;最后使用角度法读取仪表示数。实验结果表明,该算法在不同光照、天气、背景环境中均可高效准确地读取指针式仪表示数,平均读数误差率小于3.5%,可满足变电站巡检机器人的日常巡检需求。

In order to identify the reading of pointer-type meters in substations in a complex environment,a reading recognition method of pointer-type meters based on deep learning is proposed.First use the target detection algorithm YOLOv3 to detect the specific position of the meter and the meter scale value in the picture,and use the character recognition algorithm based on the LeNet-5 network to identify the specific value of the scale value;then use the semantic segmentation algorithm DeepLabv3+to segment the meter pointer area;finally use The angle method reader indicates the number.The experimental results show that the algorithm proposed in this paper can efficiently and accurately read the indications of the pointer in different lighting,weather and background environments,with an average reading error rate of less than 3.5%,which can meet the daily inspection tasks of substation inspection robots.