摘要
随着工业自动化水平的不断提升，零件缺陷检测作为保障产品质量的关键环节，其效率和精度要求日益提高。传统人工检测方法已难以满足现代工业需求，基于机器视觉的缺陷检测技术因其高效、精准的特点成为研究热点。本研究旨在探索一种基于机器视觉的零件缺陷检测方法，以实现对复杂表面零件缺陷的高精度、自动化识别。研究采用深度学习框架结合图像处理技术，设计了一种融合多尺度特征提取与自适应阈值分割的检测算法，并通过优化网络结构显著提升了小样本数据集上的模型泛化能力。实验结果表明，该方法在多种类型零件表面缺陷检测中表现出优异性能，检测准确率达到97.3%，且具备较强的鲁棒性和实时性。此外，本研究提出的数据增强策略有效缓解了工业场景中数据分布不均的问题，为小样本学习提供了新思路。总体而言，本研究不仅为零件缺陷检测提供了高效的技术方案，还为机器视觉在工业领域的广泛应用奠定了理论基础，具有重要的实际意义和应用价值。
关键词：机器视觉；零件缺陷检测；深度学习；多尺度特征提取；小样本学习
Abstract
With the continuous improvement of industrial automation, part defect detection, as a critical link in ensuring product quality, is facing increasingly higher requirements for efficiency and accuracy. Traditional manual inspection methods have become insufficient to meet modern industrial demands, while defect detection techniques based on machine vision have emerged as a research hotspot due to their high efficiency and precision. This study aims to explore a machine-vision-based method for part defect detection to achieve high-accuracy and automated recognition of defects on complex surface parts. By adopting a deep learning fr amework combined with image processing technology, a detection algorithm that integrates multi-scale feature extraction and adaptive threshold segmentation was designed. Furthermore, the generalization capability of the model on small-sample datasets was significantly improved through network architecture optimization. Experimental results demonstrate that this method exhibits superior performance in detecting various types of surface defects on different parts, achieving a detection accuracy of 97.3%, along with strong robustness and real-time capabilities. Additionally, the data augmentation strategy proposed in this study effectively alleviates the issue of uneven data distribution in industrial scenarios, offering new insights into small-sample learning. Overall, this research not only provides an efficient technical solution for part defect detection but also lays a theoretical foundation for the extensive application of machine vision in the industrial field, possessing significant practical significance and application value.
Keywords：Machine Vision; Part Defect Detection; Deep Learning; Multi-scale Feature Extraction; Few-shot Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法与技术路线 2
二、机器视觉检测基础理论 2
(一) 机器视觉基本原理 2
(二) 图像处理关键技术 3
(三) 缺陷检测算法分类与选择 3
(四) 数据采集与预处理方法 4
三、零件缺陷检测技术实现 4
(一) 缺陷特征提取方法研究 4
(二) 基于深度学习的检测模型构建 5
(三) 实时性与精度优化策略 5
(四) 检测系统硬件架构设计 6
四、实验验证与结果分析 6
(一) 实验环境与数据集构建 6
(二) 不同算法性能对比分析 7
(三) 检测准确率与效率评估 7
(四) 实际应用场景测试与改进 8
结 论 10
参考文献 11
