基于自适应全局定位算法的带钢表面缺陷检测  

Strip Surface Defect Detection Based on Adaptive Global Localization Algorithm

作　　者：王延舒 余建波[1] WANG Yan-Shu;YU Jian-Bo(School of Mechanical Engineering,Tongji University,Shanghai 201804)

机构地区：[1]同济大学机械与能源工程学院,上海201804

出　　处：《自动化学报》2024年第8期1550-1564,共15页Acta Automatica Sinica

基　　金：国家重点研发计划(2022YFF0605700);国家自然科学基金(92167107);中央高校基本业务经费项目(22120220575)资助。

摘　　要：针对热轧带钢表面缺陷检测存在的智能化水平低、检测精度低和检测速度慢等问题,提出了一种基于自适应全局定位网络(Adaptive global localization network,AGLNet)的深度学习缺陷检测算法.首先,引入一种残差网络(Residual network,ResNet)与特征金字塔网络(Feature pyramid network,FPN)集成的特征提取结构,减少缺陷语义信息在层级传递间的消失;其次,提出基于TPE(Tree-structure Parzen estimation)的自适应树型候选框提取网络(Adaptive treestructure region proposal extraction network,AT-RPN),无需先验知识的积累,避免了人为调参的训练模式;最后,引入全局定位回归算法,以全局定位的模式在复杂的缺陷检测中实现缺陷更精确定位.本文实现一种快速、准确、更智能化、更适用于实际应用的热轧带钢表面缺陷的算法.实验结果表明,AGLNet在NEU-DET热轧带钢表面缺陷数据集上的检测速度保持在11.8帧/s,平均精度达到79.90%,优于目前其他深度学习带钢表面缺陷检测算法.另外,该算法还具备较强的泛化能力.A deep learning defect detection model based on adaptive global localization network(AGLNet)is presented to solve the problems of low intelligence,low detection accuracy and slow detection speed in hot-rolled strip surface defect detection.First,the feature extraction structure is combined with residual network(ResNet)and feature pyramid network(FPN)to reduce the disappearance of defect semantic information between layers transfers.Secondly,an adaptive tree-structure region proposal extraction network(AT-RPN)based on tree-structure Parzen estimation(TPE)algorithm is proposed,which does not need the accumulation of prior knowledge,and avoids the training model by manual parameter adjustment.Finally,a global localization regression algorithm is proposed to locate defects more accurately in complex defect detection using global positioning mode.In this paper,a fast,accurate,more intelligent and more applicable algorithm for surface defects detection of hot-rolled strips is realized.The experimental results show that the detection speed of AGLNet remains 11.8 frame/s and the average accuracy is 79.90%,which is better than other deep learning algorithms for strip surface defect detection on NEUDET dataset.In addition,the algorithm has a strong generalization ability.

关 键 词：表面缺陷检测 深度学习 特征金字塔网络 自适应树型候选框提取 全局定位 

分 类 号：TG142.1[一般工业技术—材料科学与工程] TP391.41[金属学及工艺—金属材料]