Research Progress in Industrial Defect Detection Based on Deep Learning

Machine vision technology based on deep learning has important application value in industrial defect detection,which can significantly improve detection quality,efficiency and reduce labor costs compared to traditional methods.By collecting the research and application information of deep learning in defect detection in recent years,the difficulties and related solutions are summarized,and the problems are divided into two aspects:the problem of establishing defect datasets and the selection of detection models.First,at the data aspect,aiming at the problems of few samples of defects,data labeling,and low quality of data imaging,this paper correspondingly analyzes the applications of small sample learning,unsupervised,semi-supervised,self-supervised and weak-supervised learning,data augmentation,image enhancement and image translation.Then,in the selection of neural network models,according to the different types of models,they are divided into three categories:CNN based,Transformer based,and mixture model for discussion.According to different detection requirements,they are divided into three types of models:classification,detection,and segmentation.In addition,the design methods of lightweight models are summarized.Finally,the future development direction is discussed and prospected.