哈尔滨商业大学学报（自然科学版）2024，Vol.40Issue(5)：536-543,8.
基于Transformer的多尺度工件编码识别算法
Multi-scale recognition algorithm for workpiece code labels based on Transformer structure
熊新炎 1马宏伟 1张良1
作者信息
- 1. 哈尔滨商业大学 轻工学院,哈尔滨 150028
- 折叠
摘要
Abstract
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.关键词
工件编码/多尺度特征/缩放因子/注意力权重/特征融合/TransformerKey words
workpiece code labels/multi-scale features/scale factor/attention weight/feature fusion/Tranformer分类
信息技术与安全科学引用本文复制引用
熊新炎,马宏伟,张良..基于Transformer的多尺度工件编码识别算法[J].哈尔滨商业大学学报（自然科学版）,2024,40(5):536-543,8.
