引入注意力机制的偏转字符识别算法OA北大核心CSTPCD

Due to the high cost and harsh environmental limitations of traditional manual meter reading,intelligent meter reading has become the future development direction.The common types of electric meters can be mainly divided into mechanical word-wheel and liquid crystal electric meters.Among them,due to the deflection problem of mechanical word-wheel electric meters,there is a lack of character feature information in the process of character recognition,which leads to a low accuracy of this electricity meter type recognition.In order to solve this problem,this paper modifies the backbone network of YOLOv5 recognition algorithm,which improves the recognition effect of the algorithm on deflection characters of mechanical word-wheel electricity meter.Firstly,CBAM attention mechanism is introduced into the network model,which improves the feature extraction ability of the network model for deflected characters.Secondly,the Focus operation is replaced by a 6×6 convolution,and the original SPP pooling structure is replaced by a faster SPPF pooling structure to improve the operation speed of the algorithm.In order to test the recognition effect of the model,329 deflection character samples of electric meters are collected for experiments,and the overall recognition accuracy can reach 99.4%.At the same time,1 500 samples of liquid crystal electric meters are collected to test the generalization of the model,and the recognition accuracy reaches 99.6%.The experimental results show that this method solves the problem of low recognition rate of deflected characters,and verifies that the recognition model has strong generalization.