基于残差和特征分块注意力的激光打码字符分割  

Segmentation of laser coding characters based on residual and feature-grouped attention

作　　者：肖天行 吴静静 XIAO Tian-xing;WU Jing-jing(School of Mechanical Engineering,Jiangnan University,Wuxi Jiangsu 214122,China;Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology,Wuxi Jiangsu 214122,China)

机构地区：[1]江南大学机械工程学院,江苏无锡214122 [2]江苏省食品先进制造装备技术重点实验室,江苏无锡214122

出　　处：《图学学报》2023年第3期482-491,共10页Journal of Graphics

基　　金：国家自然科学基金项目(62072416,61873246)。

摘　　要：金属表面激光打码工艺易造成周围金属变性,产生灼伤等形式的大量噪声,导致字符区域背景复杂,字符对比度低及模糊问题,给后续字符识别带来困难。因此提出一种基于残差和特征分块注意力的激光打码字符特征增强与精细分割模型Res18-UNet,以突出字符信息,提高信噪比,从而有效分割目标。首先设计了注意力-残差特征提取单元,减少网络参数的同时避免网络退化,提高通道和空间的特征选择能力。其次提出特征分块注意力机制,加入了改进的特征分块空间注意力,增强微弱字符特征。此外,在上采样阶段设计了融合改进损失函数的多重监督模块,改善网络收敛能力,提高分割精度。在激光打码易拉罐罐底图像数据集上实验得到的mIoU系数、Dice系数和F1分数均优于原UNet,分别达到了0.8010,0.8895和0.9035,预测速度是原UNet的2.6倍,为12.24张/秒。实验说明,该算法能够有效地对低对比度激光打码字符进行特征增强和高精度分割,且具有在嵌入式平台上部署运行的可行性与应用前景。Laser coding on metal surface can lead to denaturation of surrounding metal and generate a significant amount of noise in the form of burns.This results in complex backgrounds in the character region,low contrast,and ambiguity of characters,which can make subsequent character recognition challenging.In response,Res18-UNet,a novel laser coding character feature enhancement and fine segmentation model,was proposed.The proposed model was based on residual and feature-grouped attention to highlight character information and improve signal-to-noise ratio,thus effectively segmenting the target.Firstly,the A-R unit was designed to reduce network parameters,effectively avoid network degradation,and improve the feature selection ability in channels and spaces.Secondly,the feature-grouped attention mechanism was proposed,and the improved spatial attention was added to enhance weak character features.In addition,a deep supervision module integrating the improved loss function was designed in the upsampling stage to improve network convergence and enhance segmentation precision.According to the experiment on the image dataset of the can bottoms with laser coding,the proposed model outperformed the original UNet model in terms of mIoU,Dice coefficient,and F1 score.Specifically,the proposed model achieved 0.8010,0.8895,and 0.9035,respectively,and attained the prediction speed 2.6 times that of the original UNet at 12.24 images/s.Experiments have proven that this algorithm can effectively enhance the features of low contrast laser coded characters and segment them with high precision,and that it has the feasibility and application prospect of deployment and operation on embedded platforms.

关 键 词：激光打码字符 图像分割 空间注意力 残差神经网络 特征分块策略 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]