基于卷积神经网络的指针式仪表识别

目前大部分研究指针式仪表识别的方法中提取指针是完全基于传统的图像处理技术,提取过程较为复杂且步骤繁多.为了有效解决指针式仪表读数识别中指针中轴线所在直线提取困难及识别精度不高等问题,本文提出了一种基于深度学习的指针式仪表的识别方法.首先用Faster R-CNN算法检测仪表圆盘,再采用基于深度学习的方法Faster R...

Recognition of Pointer Instrument Based on Convolution Neural Network

At present, most of the pointer recognition methods are based on the traditional image processing technology, and the extraction process is complicated with many steps. To effectively solve the problems of difficult pointer axis extraction and poor reading recognition accuracy of a pointer instrument, this study introduces a method of pointer instrument recognition based on deep learning. First, the Faster R-CNN algorithm is used to detect the instrument disk, and then the method based on deep learning is adopted to detect the pointer. According to the position information of the target frame, the pointer image is obtained by clipping. The final reading of the instrument is identified by binarization, thinning, Hough transform, and the least square fitting line. Compared with the traditional image processing directly on the image of the panel target frame or the original image, this method greatly reduces the interference in the process of locating the line where the pointer axis is located. The experimental results show that the average accuracy of pointer detection based on deep learning proposed in this study is up to 96.55%. It has high accuracy and stability for pointer detection of the pointer instrument under a complex background.