基于少样本学习的表面缺陷检测方法综述

打开文本图片集

DOI：10.13705/j.issn.1671-6841.2023239

Review of Surface Defect Detection Methods Based on Few-shot Learning

CHEN Li 1，2，3 ， YIN Xiangting 1，2 ， JIN Qifan 1，2 ， JIANG Xiaoheng1.²， JIU Mingyuan 1，2 ， XU Mingliang 1，2 （1.School of Computer and Artificial Intelligence， Zhengzhou University， Zhengzhou 45ooo1，China ;2. National Supercomputing Zhengzhou Center， Zhengzhou 450001， China; 3. School of PhysicalEducation （Main Campus）， Zhengzhou University， Zhengzhou 45ooO1， China）

Abstract： In some industrial scenarios，insufficient defect samples and labeling time-consuming and labor-intensive defects，limit the application of machine vision methods in surface defect detection.Technologies of industrial defect detection based on few-shot learning were introduced from three aspects ： image acquisition，image processing，and defect detection.Firstly，defect detection methods were divided into traditional surface defect detection methods and few-shot deep learning based defect detection methods.The traditional surface defect detection method was based on the manually extracted features to identify defects，which could be divided into three parts： defect segmentation，artificial feature extraction and defect recognition. Few-shot deep learning based industrial defect detection methods include data enhancement，transfer learning，model fine-tuning，semi-supervised learning，weakly supervised learning，unsupervised learning methods，etc. Secondly，some commonly used defect detection datasets and evaluation criteria of detection results were introduced.Finally，the existing problems and future research directions of few-shot learning based surface defect detection were discussed.

Key words： defect detection; few-shot learning；machine vision; deep learning

0 引言

在工业生产中，缺陷检测对于保证产品质量起着重要作用。（剩余21866字）