基于机器视觉的工件表面缺陷检测技术研究
摘 要
随着制造业的快速发展，工件表面缺陷检测在产品质量控制中扮演着至关重要的角色。传统的检测方法依赖于人工视觉或简单的光学设备，存在效率低、误检率高和成本高等问题。基于此背景，本研究旨在探索一种基于机器视觉的自动化检测技术，以提高检测精度和效率。研究采用了深度学习中的卷积神经网络（CNN）作为核心算法，结合多尺度特征提取和自适应阈值分割技术，构建了一个高效的缺陷检测模型。通过对多种工件表面缺陷数据集的训练与测试，结果表明该模型在识别精度上达到了95%以上，显著优于传统方法。此外，研究还提出了一种基于迁移学习的模型优化策略，能够在有限数据条件下实现快速部署和高效应用。
关键词：机器视觉；深度学习；卷积神经网络
RESEARCH ON THE SURFACE DEFECT DETECTION TECHNOLOGY OF WORKPIECE BASED ON MACHINE VISION
ABSTRACT
With the rapid development of manufacturing industry, the detection of workpiece surface defects plays a vital role in product quality control. Traditional detection methods rely on artificial vision or simple optical equipment, which have problems of low efficiency, high misdetection rate and high cost. Based on this background, this study aims to explore an automated detection technique based on machine vision to improve detection accuracy and efficiency. The Convolutional neural network (CNN) in deep learning is used as the core algorithm, and the multi-scale feature extraction and adaptive threshold segmentation technology are combined to build an efficient defect detection model. By training and testing a dataset of various surface defect data sets, the results show that the model achieves more than 95% recognition accuracy, which significantly outperforms the traditional methods. Moreover, the study proposes a model optimization strategy based on transfer learning that enables rapid deployment and efficient application under limited data conditions.
KEY WORDS：Machine vision; deep learning; convolutional neural network
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 研究背景及意义 1
1.2 研究现状 1
第2章 工件表面缺陷检测的机器视觉技术基础 3
2.1 机器视觉系统的组成与工作原理 3
2.2 图像采集与预处理技术 3
2.3 特征提取与分类算法 4
第3章 基于深度学习的工件表面缺陷检测方法 5
3.1 深度学习在缺陷检测中的应用概述 5
3.2 卷积神经网络模型的设计与优化 5
3.3 数据集构建与模型训练策略 6
第4章 工件表面缺陷检测系统的实现与验证 7
4.1 系统架构设计与实现 7
4.2 实验结果分析与性能评估 7
4.3 实际应用案例分析 8
第5章 结论 9
参考文献 10
致 谢 11
摘 要
随着制造业的快速发展，工件表面缺陷检测在产品质量控制中扮演着至关重要的角色。传统的检测方法依赖于人工视觉或简单的光学设备，存在效率低、误检率高和成本高等问题。基于此背景，本研究旨在探索一种基于机器视觉的自动化检测技术，以提高检测精度和效率。研究采用了深度学习中的卷积神经网络（CNN）作为核心算法，结合多尺度特征提取和自适应阈值分割技术，构建了一个高效的缺陷检测模型。通过对多种工件表面缺陷数据集的训练与测试，结果表明该模型在识别精度上达到了95%以上，显著优于传统方法。此外，研究还提出了一种基于迁移学习的模型优化策略，能够在有限数据条件下实现快速部署和高效应用。
关键词：机器视觉；深度学习；卷积神经网络
RESEARCH ON THE SURFACE DEFECT DETECTION TECHNOLOGY OF WORKPIECE BASED ON MACHINE VISION
ABSTRACT
With the rapid development of manufacturing industry, the detection of workpiece surface defects plays a vital role in product quality control. Traditional detection methods rely on artificial vision or simple optical equipment, which have problems of low efficiency, high misdetection rate and high cost. Based on this background, this study aims to explore an automated detection technique based on machine vision to improve detection accuracy and efficiency. The Convolutional neural network (CNN) in deep learning is used as the core algorithm, and the multi-scale feature extraction and adaptive threshold segmentation technology are combined to build an efficient defect detection model. By training and testing a dataset of various surface defect data sets, the results show that the model achieves more than 95% recognition accuracy, which significantly outperforms the traditional methods. Moreover, the study proposes a model optimization strategy based on transfer learning that enables rapid deployment and efficient application under limited data conditions.
KEY WORDS：Machine vision; deep learning; convolutional neural network
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 研究背景及意义 1
1.2 研究现状 1
第2章 工件表面缺陷检测的机器视觉技术基础 3
2.1 机器视觉系统的组成与工作原理 3
2.2 图像采集与预处理技术 3
2.3 特征提取与分类算法 4
第3章 基于深度学习的工件表面缺陷检测方法 5
3.1 深度学习在缺陷检测中的应用概述 5
3.2 卷积神经网络模型的设计与优化 5
3.3 数据集构建与模型训练策略 6
第4章 工件表面缺陷检测系统的实现与验证 7
4.1 系统架构设计与实现 7
4.2 实验结果分析与性能评估 7
4.3 实际应用案例分析 8
第5章 结论 9
参考文献 10
致 谢 11
