缺陷检测中小样本问题的研究进展OA

In modern industrial defect detection,deep learning methods have been widely applied but are often limited by the small-sample problem.Addressing this issue can improve detection accuracy and efficiency,reduce technical barrier,and enable small and medium-sized enterprises to lower costs and improve productivity.This paper first defines the small-sample problem and discusses its implications,then reviews the current research,analyzes the advantages and limitations of representative methods and their appli-cable scenarios,and compares costs and feasibility to offer practical guidance.Finally,future research directions and potential solu-tions are discussed,aiming to provide theoretical and practical references for addressing the small-sample problem in defect detec-tion and promoting further development and application in this field.