一种自动读取指针式仪表读数的方法

为解决变电站中自动化监控仪表读数的问题，提出基于机器学习和图像处理算法的指针式仪表自动读数方法，由仪表检测和指针识别两个阶段组成。使用全卷积网络(fully convolutional networks，FCN)对输入图像进行语义分割，以检测仪表的位置并提取仪表部分的图像。利用直方图均衡化、中值滤波和双边滤波减小光照和阴影对指针识别的干扰，并利用仿射变换矫正拍摄时的倾斜，再结合改进的霍夫变换识别仪表中指针的位置，从而计算指针角度获取读数。结果表明，对于自然场景中变电站中的指针式仪表，本研究能很好地检测出仪表并识别出指针的读数，对于光照和阴影等干扰具有良好的鲁棒性，可以显著减少变电站巡检人员的工作量，提高工作效率。

An automatic reading method for pointer meter

An automatic reading method for automatically monitoring pointer meter in substation was proposed based on the machine learning and image processing algorithms, which was consisted of two stages: meter detection and pointer recognition. The position of the meter in the input image was detected by using the fully convolutional networks, and then the patch of the meter was extracted. The interference of illumination and shadow on the pointer recognition was reduced by using histogram equalization, median filtering and bilateral filtering, and the tilt of shooting was rectified by using the affine transformation. The position of the pointer was detected via the improved Hough transform. The reading was obtained by computing the angle of the pointer. The results showed that the method could detect the pointer meter and recognize the reading accurately for the pointer instrument in the substation. The method showed good robustness to the disturbances such as illumination and shadow, which could significantly reduce the substation inspection personnel workload and improve the work efficiency.