Detection and recognition of tire imprint characters based on deep learning
When assembling tires and wheels on an automated assembly line,it is necessary to detect and identify the imprint character strings on the tire surface,so as to obtain the tire brand,model,size,and production year and week number,which can be used to manage the tire information and monitor the flow direction of tire.Aiming at the detection and recognition of imprint character strings on the tire surface,a method for detecting and recognizing key characters on the tire tread based on deep learning is proposed.An automated detection and recognition platform for programmable logic controller(PLC),industrial computer,and industrial camera is built.The collected images are pre-processed through Hough transform and coordinate transformation,and then the target character string position is detected based on the improved faster region-based convolutional neural network(Faster RCNN)algorithm,and then the detected target character string is recognized by convolutional recurrent neural network(CRNN),and the coding rules are used to verify the recognition result to im-prove the accuracy of the recognition result.Experimental results show that the improved algorithm has an accuracy rate of over 97.0％when detecting and recognizing tire imprint character strings,which meets the needs of industrial production applications.
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