摘要
随着工业4.0的推进和智能制造技术的发展，传统工业检测手段已难以满足高精度、高效率的质量控制需求，基于机器视觉的智能检测技术逐渐成为研究热点与应用趋势本研究旨在设计并实现一套基于机器视觉的工业检测系统，以解决复杂工业场景中目标识别、缺陷检测及质量评估等关键问题通过融合先进的图像处理算法与深度学习模型，系统能够实现实时数据采集、多维度特征提取以及智能化决策在方法层面，研究采用了模块化设计思路，包括图像获取与预处理、目标定位与分割、缺陷分类与评估等核心环节，并引入卷积神经网络优化特征表达能力同时，为提升系统的鲁棒性与适应性，研究提出了一种自适应阈值调整机制，能够在不同光照条件和材质特性下保持稳定性能实验结果表明，该系统在多种工业应用场景中表现出优异的检测精度与运行效率，相较于传统方法，其准确率提升了约15%，误检率降低了约20%此外，系统具备良好的可扩展性，能够根据具体任务需求灵活配置功能模块综上所述，本研究提出的基于机器视觉的工业检测系统不仅有效解决了当前工业检测中的技术瓶颈，还为未来智能化制造提供了重要的技术支持与实践参考
关键词：机器视觉；工业检测；深度学习
Abstract
With the advancement of Industry 4.0 and the development of intelligent manufacturing technologies, traditional industrial inspection methods are increasingly unable to meet the high-precision and high-efficiency requirements for quality control. As a result, intelligent inspection techniques based on machine vision have gradually become a research hotspot and application trend. This study aims to design and implement an industrial inspection system based on machine vision to address key challenges such as target recognition, defect detection, and quality assessment in complex industrial scenarios. By integrating advanced image processing algorithms with deep learning models, the system achieves real-time data acquisition, multi-dimensional feature extraction, and intelligent decision-making. At the methodological level, a modular design approach is adopted, encompassing core stages such as image acquisition and preprocessing, target localization and segmentation, and defect classification and evaluation. A convolutional neural network is introduced to optimize feature representation capabilities. To enhance the robustness and adaptability of the system, an adaptive threshold adjustment mechanism is proposed, ensuring stable performance under varying lighting conditions and material characteristics. Experimental results demonstrate that the system exhibits superior detection accuracy and operational efficiency across multiple industrial application scenarios, with an accuracy improvement of approximately 15% and a false detection rate reduction of about 20% compared to traditional methods. Moreover, the system demonstrates excellent scalability, allowing flexible configuration of functional modules according to specific task requirements. In summary, the machine vision-based industrial inspection system proposed in this study not only effectively addresses current technical bottlenecks in industrial inspection but also provides crucial technical support and practical references for future intelligent manufacturing.
Keywords：Machine Vision; Industrial Inspection; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 工业检测系统的发展背景 1
(二) 机器视觉在工业检测中的意义 1
(三) 国内外研究现状分析 1
(四) 本文研究方法与技术路线 2
二、系统需求分析与设计框架 2
(一) 工业检测系统的功能需求 2
(二) 机器视觉技术的核心要求 3
(三) 系统整体架构设计 3
(四) 关键模块的功能划分 4
(五) 设计原则与性能指标 4
三、核心算法与技术实现 5
(一) 图像采集与预处理方法 5
(二) 特征提取与模式识别技术 6
(三) 缺陷检测算法的设计与优化 6
(四) 实时性与精度的平衡策略 7
(五) 算法测试与结果分析 7
四、系统集成与应用验证 8
(一) 硬件平台的选择与搭建 8
(二) 软件系统的开发与调试 8
(三) 实际应用场景的适配性分析 9
(四) 检测效率与准确率评估 9
(五) 系统优化与改进建议 10
结 论 11
参考文献 12
随着工业4.0的推进和智能制造技术的发展，传统工业检测手段已难以满足高精度、高效率的质量控制需求，基于机器视觉的智能检测技术逐渐成为研究热点与应用趋势本研究旨在设计并实现一套基于机器视觉的工业检测系统，以解决复杂工业场景中目标识别、缺陷检测及质量评估等关键问题通过融合先进的图像处理算法与深度学习模型，系统能够实现实时数据采集、多维度特征提取以及智能化决策在方法层面，研究采用了模块化设计思路，包括图像获取与预处理、目标定位与分割、缺陷分类与评估等核心环节，并引入卷积神经网络优化特征表达能力同时，为提升系统的鲁棒性与适应性，研究提出了一种自适应阈值调整机制，能够在不同光照条件和材质特性下保持稳定性能实验结果表明，该系统在多种工业应用场景中表现出优异的检测精度与运行效率，相较于传统方法，其准确率提升了约15%，误检率降低了约20%此外，系统具备良好的可扩展性，能够根据具体任务需求灵活配置功能模块综上所述，本研究提出的基于机器视觉的工业检测系统不仅有效解决了当前工业检测中的技术瓶颈，还为未来智能化制造提供了重要的技术支持与实践参考
关键词：机器视觉；工业检测；深度学习
Abstract
With the advancement of Industry 4.0 and the development of intelligent manufacturing technologies, traditional industrial inspection methods are increasingly unable to meet the high-precision and high-efficiency requirements for quality control. As a result, intelligent inspection techniques based on machine vision have gradually become a research hotspot and application trend. This study aims to design and implement an industrial inspection system based on machine vision to address key challenges such as target recognition, defect detection, and quality assessment in complex industrial scenarios. By integrating advanced image processing algorithms with deep learning models, the system achieves real-time data acquisition, multi-dimensional feature extraction, and intelligent decision-making. At the methodological level, a modular design approach is adopted, encompassing core stages such as image acquisition and preprocessing, target localization and segmentation, and defect classification and evaluation. A convolutional neural network is introduced to optimize feature representation capabilities. To enhance the robustness and adaptability of the system, an adaptive threshold adjustment mechanism is proposed, ensuring stable performance under varying lighting conditions and material characteristics. Experimental results demonstrate that the system exhibits superior detection accuracy and operational efficiency across multiple industrial application scenarios, with an accuracy improvement of approximately 15% and a false detection rate reduction of about 20% compared to traditional methods. Moreover, the system demonstrates excellent scalability, allowing flexible configuration of functional modules according to specific task requirements. In summary, the machine vision-based industrial inspection system proposed in this study not only effectively addresses current technical bottlenecks in industrial inspection but also provides crucial technical support and practical references for future intelligent manufacturing.
Keywords：Machine Vision; Industrial Inspection; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 工业检测系统的发展背景 1
(二) 机器视觉在工业检测中的意义 1
(三) 国内外研究现状分析 1
(四) 本文研究方法与技术路线 2
二、系统需求分析与设计框架 2
(一) 工业检测系统的功能需求 2
(二) 机器视觉技术的核心要求 3
(三) 系统整体架构设计 3
(四) 关键模块的功能划分 4
(五) 设计原则与性能指标 4
三、核心算法与技术实现 5
(一) 图像采集与预处理方法 5
(二) 特征提取与模式识别技术 6
(三) 缺陷检测算法的设计与优化 6
(四) 实时性与精度的平衡策略 7
(五) 算法测试与结果分析 7
四、系统集成与应用验证 8
(一) 硬件平台的选择与搭建 8
(二) 软件系统的开发与调试 8
(三) 实际应用场景的适配性分析 9
(四) 检测效率与准确率评估 9
(五) 系统优化与改进建议 10
结 论 11
参考文献 12
