摘 要
随着工业自动化和智能制造的快速发展，机械零部件的质量检测已成为提升生产效率和产品可靠性的关键环节。传统的人工检测方法存在效率低、主观性强及难以满足高精度需求等局限性，因此基于机器视觉的检测技术逐渐成为研究热点。本研究旨在探索一种高效、精准且适应性强的机械零部件检测方案，以解决复杂环境下零部件表面缺陷、尺寸偏差及装配误差等问题。研究采用多模态图像采集与处理技术，结合深度学习算法对零部件特征进行提取与分类，并通过优化的卷积神经网络模型实现对微小缺陷的高灵敏度识别。同时，引入自适应阈值调整机制以应对不同光照条件和材质差异带来的干扰。实验结果表明，该方法在检测准确率、速度及鲁棒性方面均优于传统方法，特别是在亚毫米级缺陷检测中表现出显著优势。此外，所提出的检测系统具备良好的可扩展性和兼容性，能够适配多种类型的机械零部件检测任务。本研究的主要创新点在于将深度学习与传统机器视觉技术深度融合，构建了一种智能化、自动化的检测框架，为工业领域的质量控制提供了新思路和技术支撑。研究成果不仅提升了检测效率和精度，还为未来智能检测系统的开发奠定了理论与实践基础。
关键词：机械零部件检测；深度学习；机器视觉
Abstract: With the rapid development of industrial automation and smart manufacturing, the quality inspection of mechanical components has become a critical factor in enhancing production efficiency and product reliability. Traditional manual inspection methods are limited by low efficiency, strong subjectivity, and an inability to meet high-precision requirements, leading to the growing prominence of machine vision-based inspection technologies. This study aims to explore an efficient, precise, and adaptable inspection solution for mechanical components, addressing issues such as surface defects, dimensional deviations, and assembly errors under complex conditions. A multimodal image acquisition and processing technique is employed in conjunction with deep learning algorithms for feature extraction and classification of components, while an optimized convolutional neural network model enables highly sensitive identification of minute defects. Additionally, an adaptive threshold adjustment mechanism is introduced to mitigate interference caused by varying lighting conditions and material differences. Experimental results demonstrate that this method surpasses traditional approaches in terms of detection accuracy, speed, and robustness, particularly exhibiting significant advantages in sub-millimeter defect detection. Furthermore, the proposed inspection system demonstrates excellent scalability and compatibility, capable of adapting to various types of mechanical component inspection tasks. The primary innovation of this research lies in the deep integration of deep learning with conventional machine vision technology, establishing an intelligent and automated inspection fr amework that provides new insights and technical support for quality control in industrial applications. The research not only enhances inspection efficiency and precision but also lays a theoretical and practical foundation for the development of future intelligent inspection systems.
Keywords: Mechanical Component Inspection; Deep Learning; Machine Vision
目 录
1绪论 1
1.1机械零部件检测技术的研究背景 1
1.2基于机器视觉的检测技术意义 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2机器视觉在机械零部件检测中的基础理论 2
2.1机器视觉的基本原理与框架 2
2.2图像处理技术在检测中的应用 3
2.3关键算法对检测性能的影响 3
2.4数据采集与预处理方法研究 4
2.5理论支撑与技术可行性分析 4
3机械零部件检测系统的设计与实现 5
3.1检测系统的整体架构设计 5
3.2视觉传感器的选择与优化 5
3.3检测流程的关键环节分析 6
3.4系统集成与功能验证 6
3.5性能评估与改进策略 7
4实验验证与结果分析 7
4.1实验平台搭建与数据获取 7
4.2不同零部件检测效果对比 8
4.3错误检测原因分析与改进 8
4.4检测效率与精度的综合评价 9
4.5实验结果 9
结论 11
参考文献 12
致 谢 13
