Surface Character Recognition and Character Defect Detection of Lithium Bat-teries Based on Deep Learning
Aiming at the shortcomings of manual detection in character recognition and defect detection of soft-pack lithium battery surface coding,a character recognition method based on CnOCR and a character defect detection method based on improved YOLOv8 model are proposed.The method first uses the CnStd algorithm to locate the character area,and uses the YOLOv8 model to train the characters to detect defective characters.According to the characteristics of the character area,image enhancement,binarization and character segmentation are carried out,and the CnOCR model is used for character recognition.The deep learning method improves the accuracy of character recognition and defect detection,and ensures the recognition and detection speed of the entire detection system.The experimental results show that the character recognition rate is above 96%and the character defect detection rate is above 94%,which meets the production needs of the lithium battery automated production line.
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