基于文本特征及二次矫正的指针式仪表自动读数算法

现有的指针式仪表读数算法常通过检测仪表的刻度进行示数识别，但仪表图像中的刻度包含的特征较少，从而容易出现误检测。针对此问题，提出了一种新的指针式仪表自动读数算法，该算法通过选取较大区域的图像特征大幅度地提高了仪表读数识别的鲁棒性。由于指针刻度值文本是各类仪表具有的共同部分且具有远多于刻度图像的特征，因此所提算法以刻度值文本作为识别依据，首先通过卷积神经网络检测仪表图像中的刻度值文本，并使用其位置坐标来拟合仪表的圆心，在得到圆心的基础上通过极坐标变换及图像二次矫正将圆弧形的刻度区域转换为水平直线型的区域，同时被识别的文本值也用于改进距离判读法。该方法与其他读数算法的比较结果表明，该算法具有较高的读数准确率，引用误差在0.5%以下，且在复杂拍摄条件下具有更高的鲁棒性。

An automatic reading algorithm of pointer meterbased on text feature and secondary correction

Existing pointer meter reading algorithms usually detect the scale of the meter to identify the value. However, in the meter image, the scale contains less features, which is prone to misdetection. To solve this problem, a new automatic reading algorithm of pointer meter is proposed. This algorithm greatly improves the robustness of meter reading recognition by selecting image features of a larger area. Because the pointer scale value text is a common part of various meters and has far more image features than scale, the proposed algorithm uses the scale value text as the recognition basis. Firstly, the convolutional neural network detects the scale value text in the meter image, and uses its position coordinates to fit the center of the meter. The secondary image correction converts the arc-shaped scale area into a horizontal straight-line area. At the same time, the recognized text value is also used to improve the distance interpretation method. This method is compared with other reading algorithms. The comparison experiment proves, this algorithm has a high reading accuracy rate, its reference error is less than 0.5%, and it has higher robustness under complex shooting conditions.