Multi-scale recognition algorithm for workpiece code labels based on Transformer structure
This paper proposed a multi-scale workpiece encoding recognition algorithm based on the Transformer structure,aiming to address the existing challenges and limitations in current workpiece encoding recognition.Provided an overview of the application of machine vision in workpiece encoding recognition and relevant knowledge regarding the Transformer structure.By deeply exploring the roles of multi-scale features and Transformer modules in workpiece encoding recognition,this paper details the implementation methods for multi-scale feature extraction and fusion,and prominently introduced the optimization strategies for the Transformer module.This paper obtained a set of features at different scales and levels through convolution and pooling operations.Subsequently,an innovative scaling factor was introduced to adjust the attention weights in the Transformer module to more accurately capture and fuse features of different scales.And proposed a new method for calculating the scaling factor,which directly depends on the information of Query and Key,and can more intuitively reflect the importance of different scale features in attention computation.Additionally,the results of which demonstrate that our method exhibits higher accuracy and robustness when dealing with multi-scale workpiece encoding features,effectively enhancing the performance of workpiece encoding recognition.
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