自动化学报2024，Vol.50Issue(8)：1550-1564,15.DOI:10.16383/j.aas.c210467
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
摘要
Abstract
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-struc-ture 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 NEU-DET 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Key words
Surface defects detection/deep learning/feature pyramid network(FPN)/adaptive tree-structure re-gion proposal extraction/global localization引用本文复制引用
王延舒,余建波..基于自适应全局定位算法的带钢表面缺陷检测[J].自动化学报,2024,50(8):1550-1564,15.基金项目
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Supported by National Key Research and Development Pro-gram of China(2022YFF0605700),National Natural Science Foundation of China(92167107),and Fundamental Research Funds for the Central Universities(22120220575) （2022YFF0605700）
