Instrument recognition method based on Faster R-CNN

In view of the complex background of the instrument recognition system, insensitive to small targets, and low detection accuracy, an automatic instrument identification method combining Feature Fusion Pyramid (FPN) and Faster R-CNN network is proposed in this paper. First, FPN and the RPN of the Faster R-CNN network are used to combine the positioning of the dial and the pointer area, and to classify multiple types of meters. In addition, in order to balance the positive and negative samples of the meter images and improve the detection accuracy, the Focal Loss loss function is combined with the RPN network to train the data set. The FPN-based image segmentation is performed on the pointer area. The FPN network and deconvolution are combined to improve the accuracy of the pointer area segmentation, and finally the pointer is fit to obtain the pointer deflection angle and obtain the meter reading.The experimental results show that the accuracy of the proposed method reaches 94.25%. Compared with the traditional algorithm, the proposed method not only has high detection accuracy, but also has stronger practicability.