摘 要
针对当前食品包装缺陷检测效率低、人工成本高及传统机器视觉方法识别精度不足的问题，本文设计了一种基于深度学习的食品包装缺陷智能视觉检测系统。该系统旨在提升检测速度与准确率，满足现代食品工业自动化生产对质量控制的高标准需求。研究融合了多尺度卷积神经网络与迁移学习技术，构建适用于小样本条件下的缺陷识别模型，并引入注意力机制增强对关键区域的特征提取能力。实验采用实际生产线采集的食品包装图像数据集，涵盖多种常见缺陷类型，在GPU嵌入式平台上完成模型训练与推理部署。结果表明，系统在测试集上的平均检测精度达到98.7%，单张图像处理时间低于45ms，显著优于传统方法。本研究的创新点在于提出一种轻量化且适应性强的网络结构，有效解决了食品包装表面纹理复杂、缺陷样本稀缺带来的检测难题。系统的实现为食品包装质量检测提供了高效、稳定的技术支持，具有良好的工程应用前景。
关键词：深度学习；食品包装缺陷检测；多尺度卷积神经网络；迁移学习；注意力机制
Design of an Intelligent Visual Inspection System for Food Packaging Defects
Abstract: To address the issues of low efficiency, high labor costs, and insufficient recognition accuracy in traditional machine vision methods for food packaging defect detection, this paper designs an intelligent visual detection system based on deep learning. The system aims to enhance detection speed and accuracy, meeting the high-standard quality control requirements of modern automated food production. The research integrates a multi-scale convolutional neural network with transfer learning to build a defect recognition model suitable for small-sample conditions, while incorporating an attention mechanism to strengthen feature extraction capabilities in key regions. Experiments are conducted using a dataset of food packaging images collected from actual production lines, covering various common defect types, with model training and inference deployed on a GPU-based embedded platform. Results show that the system achieves an average detection accuracy of 98.7% on the test set, with processing time per image below 45 ms, significantly outperforming traditional approaches. The main innovation lies in proposing a lightweight and adaptive network structure that effectively addresses challenges posed by complex surface textures and limited defect samples. The developed system provides efficient and stable technical support for food packaging quality inspection, demonstrating promising prospects for engineering applications.
Keywords: Deep Learning; Food Packaging Defect Detection; Multi-scale Convolutional Neural Network; Transfer Learning; Attention Mechanism
目 录
1绪论 1
1.1研究背景和意义 1
1.2研究现状 1
1.3本文研究方法 1
2系统总体架构与功能模块设计 2
2.1食品包装缺陷检测系统的整体框架构建 2
2.2图像采集与预处理模块的设计实现 2
2.3缺陷识别与分类算法集成方案 3
2.4系统控制与人机交互界面设计 3
3关键技术与算法优化研究 3
3.1基于深度学习的包装缺陷图像识别方法 3
3.2多尺度特征融合在缺陷检测中的应用 4
3.3图像分割与边缘检测算法性能比较 4
3.4模型轻量化与实时性提升策略 5
4系统集成与实验验证分析 5
4.1视觉检测系统的硬件平台搭建 5
4.2系统软件流程与数据通信机制 6
4.3实验设计与样本数据集构建 6
4.4系统性能评估与结果分析 6
结论 7
参考文献 8
致 谢 9
