首页|基于Transformer的陶瓷轴承表面缺陷检测方法

基于Transformer的陶瓷轴承表面缺陷检测方法

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针对传统机器视觉检测方法中,由于陶瓷轴承滚动体表面曲率大、对比度低,表面成像模糊导致后续缺陷检测精度低的问题,提出一种基于Transformer的超分辨率残差网络.首先,网络使用残差学习策略,通过预测模糊图像与清晰图像之间的差值,实现超分辨率任务;其次,在网络上前端插入通道注意力模块和空间注意力模块并改进L2 多头自注意力模块,以增强图像纹理、改善梯度爆炸问题;最后,针对超分辨率重建任务,提出一种两阶段训练策略优化训练过程.自建陶瓷轴承表面缺陷数据集上的大量实验结果表明,所提出网络模型在客观指标与主观评价上均优于MSESR-GAN、VSDR等超分辨率算法,重建图像SSIM为0.939,PSNR为36.51 dB.
Ceramic Bearing Surface Defect Detection Method Based on Transformer
A Transformer based super-resolution residual network is proposed for the problem of low accu-racy of subsequent defect detection due to blurred surface imaging caused by large curvature and low con-trast of ceramic bearing roller surface in traditional machine vision inspection methods.Firstly,the network uses a residual learning strategy to achieve the super-resolution task by predicting the difference between blurred and clear images;Secondly,a channel attention module and a spatial attention module are inserted in the front end of the network and the L2 multi-head self-attention module is improved to enhance the image texture and improve the gradient explosion problem;Finally,a two-stage training strategy is proposed to op-timize the training process for the super-resolution reconstruction task.The extensive experimental results on the self-built ceramic bearing surface defect dataset show that the proposed network model outperforms su-per-resolution algorithms such as MSESRGAN and VSDR in both objective metrics and subjective evalua-tion,with a reconstructed image SSIM of 0.939 and PSNR of 36.51 dB.

Si3N4 ceramic bearingsuper-resolution reconstructionTransformerimage restorationimge enhancement

安冬、胡荣华、王丽艳、邵萌、李新然、刘则通

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沈阳建筑大学机械工程学院,沈阳 110168

Si3N4陶瓷轴承 超分辨率重建 Transformer 图像恢复 图像增强

国家自然科学基金面上项目辽宁省教育厅项目

51975130LJKMZ20220915

2024

组合机床与自动化加工技术
大连组合机床研究所 中国机械工程学会生产工程分会

组合机床与自动化加工技术

CSTPCD北大核心
影响因子:0.671
ISSN:1001-2265
年,卷(期):2024.(2)
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