基于自适应全局定位算法的带钢表面缺陷检测OA北大核心CSTPCD

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.