深度学习中单阶段金属表面缺陷检测算法优化综述

doi:10.3778/j.issn.1002-8331.2408-0098

摘要/Abstract

摘要： 金属表面的划痕、凹坑、波纹等缺陷会直接影响产品的质量。传统的检测方法耗时耗力，准确性受限于操作人员的经验和技能。近年来，深度学习技术在图像识别领域的突破性进展为金属表面缺陷检测提供了新的解决方案，基于深度学习的金属表面缺陷检测方法在检测精度和速度方面取得了显著成效。为了便于金属表面缺陷检测算法的研究，综合分析了单阶段深度学习算法在金属表面缺陷检测中的优化方法及应用。介绍了目前常用的金属表面缺陷数据集和算法评价指标；总结了目标检测算法的发展史以及单阶段目标检测算法的基本概念和典型模型；从数据增强、特征的提取与融合、锚框优化三个方面，对比总结了不同算法不同优化方式的优缺点，并研究了金属表面缺陷检测算法的轻量化；从多模态融合、大数据应用技术、现实与虚拟结合三个方面对金属表面缺陷检测算法的未来研究方向进行了展望。

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

Abstract: Scratches, pits, ripples and other defects on the metal surface will directly affect the quality of the product. Traditional 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 remarkable 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 introduced. 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 direction of metal surface defect detection algorithm is prospected from three aspects：multi-mode fusion, big data application technology, reality and virtual combination.

Key words: metal surface defect detection, deep learning, one-stage target detection algorithm, model optimization

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.

DONG Jiadong, GUO Qinghu, CHEN Lin, SANG Feihu. Review on Optimization Algorithms for One-Stage Metal Surface Defect Detection in Deep Learning[J]. Computer Engineering and Applications, 2025, 61(4): 72-89.

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