Automatic inspection reading of instrument based on vision is an important research content. The automatic reading of theindustrial meter is usually determined by detecting the position of the pointer. For meters of different specifications?and categories,thespecific readings represented by the pointer pointing to the same position are different. Therefore,?
it is an important prerequisite forautomatic reading to identify the detailed categories of meters in advance. In order to improve the convenience and accuracy of automaticmeter reading,a fine-grained meters classification method based on discriminant attention fusion is proposed. Firstly,the meter panel isroughly extracted by YOLOv5,and then the proposed model is used for fine-grained recognition of the meter panel. There is no need tomark significant features,and the discriminant attention module is added to the feature extractor to supplement the guiding role of shallowspace and location information on the fine granularity classification of meters. The discriminant granularity attention map generated isbilinear fused with the feature map output from the last layer of the backbone network to generate the feature matrix. Orthogonal loss isintroduced to constrain the generated feature matrix. The meter fine-grained classification data set is built. Theoretical analysis and experimental results show that the proposed meter fine-grained classification method effectively improves the performance of the meter fine-grained classification,and provides a guarantee for the subsequent intelligent and accurate reading of the meter.