Optimized Design of Instrument Recognition Based on CNN Model

Intelligent recognition of instrument features plays an important role in automation management and overhaul and also facilitates the realization of accurate reading of key parameters in complex environments. The instrument dial intelligent recognition system proposed in this paper consists of geometry correction, pointer segmentation, and reading recognition modules. Combining the idea of the GhostNet model to improve the structure of the backbone network of the Mask RCNN model, the attention mechanism is introduced into the U-Net model, and the minimum outer rectangle method is used for reading recognition. Under different viewpoint rotation angles, the recognition errors of this paper’s method are relatively stable, and they are less than 1%. The region segmentation precision, recall, and accuracy are 99.39%, 99.05%, and 98.38%, respectively. The average error of the recognition results is only -0.04°C, which is satisfactory for instrument recognition.