Reading Recognition Method for Pointe Meters Based on Irregula Object Detection
Aiming at the problems of multiple processes,large cumulative errors,and poor recognition performance for tilted images in current pointer instrument reading recognition methods,a pointer instrument reading recognition method based on irregular object detection network was proposed.Firstly,a calibration network structure was constructed,irregular target vertex coordinates was extracted,and perspective transformation was automatically performed on the image to enhance the learning performance of the overall network for tilted samples.Subsequently,convolutional neural networks were used to directly extract image features,a-chieving the regression task of reading information and reducing method steps.Finally,the model was aggregated to enable tilt cal-ibration and reading recognition tasks to be learned and implemented together through a backpropagation neural network.The ex-periment shows that the method improves the reading recognition accuracy of inclined instrument images,with a short process and high recognition efficiency.
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