摘要
产品表面缺陷检测任务的重点是对产品表面图像中的异常缺陷区域进行自动检测和分割。然而实际应用中,由于噪声的退化影响和缺陷类型的复杂多样,产品表面缺陷检测仍然是一项具有挑战性的任务。为了应对这些问题,提出了产品表面缺陷检测的多通路阈值收缩融合网络。在各尺度通路中,为了降低噪声干扰,该网络设计了自适应阈值收缩去噪模块,通过双支路自主学习水平和垂直方向的收缩阈值,去除特征中的干扰噪声并且保留有效背景信息,从而实现自适应去噪。为了更准确定位缺陷对象,设计了上下文三维注意力融合模块,通过水平聚合和垂直聚合生成三维注意力图,增强异常区域特征。最终将平行的多尺度特征融合,实现对不同尺度以及不同类型缺陷的有效检测。将所构建模型在SD-900和MVTec-AD数据集上与最新的8种方法进行比较,实验结果表明该模型能够有效提升检测精度,并能够对噪声干扰保持鲁棒性,消融实验也验证了自适应阈值收缩去噪模块和上下文三维注意力模块融合的有效性。
The task of product surface defect detection focuses on the automatic detection and segmentation of abnormal defect areas.In practice,the detection of product surface defects remains a challenging task due to the degrading effects of noise and the complexity and variety of defect types.To cope with these problems,this paper proposes a multi-stream threshold shrinkage and fusion network for product surface defect detection.In each stream of different scales,in order to cope with noise corruption,the proposed network configures an adaptive threshold shrinkage denoising module.This module can autonomously learn the horizontal and vertical shrinkage thresholds in the dual branches,and remove the interference noise from the features while retaining the effective background information,therefore realizing adaptive denoising.In order to locate the defective object more accurately,a contextual 3D attention fusion module is designed to generate 3D attention maps by horizontal and vertical aggregation to enhance the abnormal region features.Finally,parallel multi-scale features are fused to achieve effective detection of different scales and different types of defects.This paper compares the constructed model on SD-900 and MVTec-AD datasets with the latest eight methods.The experimental results show that the model in this paper can effectively improve the detection accuracy and maintain robustness to noise interference,and the ablation experiments also verify the effectiveness of the adaptive threshold shrinkage denoising module and the contextual 3D attention fusion module.
作者
耿玉标
岳志远
闫麒名
孙玉宝
GENG Yubiao;YUE Zhiyuan;YAN Qiming;SUN Yubao(Jiangsu Key Laboratory of Big Data Analysis Technology,Jiangsu Collaborative Innovation Center on Atmospheric Environment&Equipment Technology,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《计算机工程与应用》
CSCD
北大核心
2023年第10期162-170,共9页
Computer Engineering and Applications
基金
国家自然科学基金(U2001211)。
关键词
缺陷检测
多尺度融合网络
自适应阈值去噪
上下文三维注意力
defect detection
multi-scale fusion network
self-adaptive threshold denoising
contextual 3D attention
