钢材表面缺陷检测研究综述

Survey of Steel Surface Defect Detection Research

钢材是工业领域不可或缺的原材料,表面缺陷严重影响钢材质量。传统钢材表面缺陷检测方法精度低、速度慢、劳动强度大,无法满足实际生产需求。近年来深度学习技术发展迅速,其能充分挖掘目标图像底层特征信息,给钢材缺陷检测带来了新的解决方案。综述近年钢材表面缺陷检测方法相关文献,简述传统检测方法的原理及其适用性,分析深度学习检测模型的结构与特点,并对目前该领域存在的一些技术难点进行总结,对未来发展趋势进行展望。

Steel is an indispensable raw material in the industrial field,and surface defects seriously affect the quality of steel.Traditional steel surface defect detection methods have low accuracy,slow speed,and high labor intensity,which cannot meet actual production needs.In recent years,deep learning technology has developed rapidly,which can fully explore the underlying feature information of target images,bringing new solutions to steel defect detection.Summarize the relevant literature on steel surface defect detection methods in recent years,briefly describe the principles and applicability of traditional detection methods,analyze the structure and characteristics of deep learning de⁃tection models,and summarize some technical difficulties in the current field,and look forward to future development trends.