巡检机器人中指针式仪表示数的自动识别方法

针对巡检机器人室外自主识别仪表示数易受到光照影响的问题,在研究了基于二维伽马函数的仪表图像光照不均匀自适应校正算法的基础上,提出了基于最大稳定极值区域(MSER)提取指针区域的算法。首先,通过三尺度高斯函数提取光照分量,构造二维伽马函数自动地调整图像反光区域或过暗区域的亮度;然后,通过MSER的两次稳定区域检测提取指针区域;接着,以指针通过仪表轴心为条件,用细化算法和累计概率霍夫变换(PPHT)精确地定位到指针,提高了定位直线的准确度;最后,通过PPHT检测的直线两个端点与轴心位置比较,直接可以判断指针指向,更加方便了计算示数。实验结果表明,所提的仪表示数识别方法能够适应不同光照下、不同类型仪表的指针定位,且识别示数的正确率达到94%以上。

Automatic recognition method of pointer meter for inspection robots

In the outdoor working environment of inspection robots, recognizing the number of meter was susceptible to illumination. An adaptive adjustment algorithm for meter images based on 2D-Gamma function was studied. Then an algorithm based on Maximally Stable Extremal Region (MSER) was proposed to extract the pointer. Firstly, the reflection component was extracted by three-scale Gaussian functions, 2D Gamma function was constructed to automatically adjust brightness of the reflected or overshadowed region of image. Secondly, the pointer region was extracted through two MSER detections. Thirdly, on the condition that the pointer passed through the axis of dial, the pointer was precisely positioned by thinning algorithm and Progressive Probabilistic Hough Transform (PPHT) to improve the accuracy of positioning lines. Finally, on basis of comparing the positions of two endpoints by PPHT with axis, the direction of pointer was directly determined, thus calculating the number was more convenient. The experimental results show that the proposed method can deal with different types of meters under different lighting conditions. Moreover, the correct rate of identification reaches over 94%.