计算机工程与应用2025，Vol.61Issue(4)：72-89,18.DOI:10.3778/j.issn.1002-8331.2408-0098
深度学习中单阶段金属表面缺陷检测算法优化综述
Review on Optimization Algorithms for One-Stage Metal Surface Defect Detection in Deep Learning
摘要
Abstract
Scratches,pits,ripples and other defects on the metal surface will directly affect the quality of the product.Tra-ditional detection methods are time consuming,and the accuracy is limited by the operator's experience and skills.In recent years,breakthroughs of deep learning technology in the field of image recognition have provided new solutions for metal surface defect detection,and the deep learning-based metal surface defect detection method have achieved remark-able results in terms of detection accuracy and speed.In order to facilitate the research of metal surface defect detection algorithm,the optimization method and application of one-stage deep learning algorithm in metal surface defect detection are comprehensively analyzed.The commonly used metal surface defect datasets and algorithm evaluation indexes are intro-duced.The development history of object detection algorithms,the basic concepts and typical models of one-stage object detection algorithms are summarized.From three aspects of data enhancement,feature extraction and fusion,anchor frame optimization,the advantages and disadvantages of different algorithms and different optimization methods are compared and summarized,and the light weight of metal surface defect detection algorithm is also studied.The future research direc-tion of metal surface defect detection algorithm is prospected from three aspects:multi-mode fusion,big data applica-tion technology,reality and virtual combination.关键词
金属表面缺陷检测/深度学习/单阶段目标检测算法/模型优化Key words
metal surface defect detection/deep learning/one-stage target detection algorithm/model optimization分类
信息技术与安全科学引用本文复制引用
董甲东,郭庆虎,陈琳,桑飞虎..深度学习中单阶段金属表面缺陷检测算法优化综述[J].计算机工程与应用,2025,61(4):72-89,18.基金项目
国家自然科学基金(62205005) （62205005）
安徽省高校科研计划重大项目(2024AH040174). （2024AH040174）
