基于机器视觉和深度学习的包装缺陷检测系统

J. Sa, Zhihao Li, Qijun Yang, Xuan Chen
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引用次数: 3

摘要

准确、高效地检测包装缺陷对提高产品质量具有重要意义。根据图像的特点,利用OpenCV对来自破损包装的图像进行预处理。处理后的数据与深度学习相结合,并基于神经网络模型ResNet。同时将处理后的图像数据发送到卷积神经网络(CNN)进行模型训练。我们建立了产品包装检测系统。该检测系统为包装缺陷自动检测提供了解决方案,实现了对产品包装的快速、准确检测。
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Packaging Defect Detection System Based on Machine Vision and Deep Learning
Detecting packaging defection with high accuracy and efficiency is of great significance in product quality. We use OpenCV to preprocess images which come from damaged package according to characteristics of the image. The processed data is combined with deep learning and based on neural network model ResNet. Meanwhile the processed image data is sent to a convolutional neural network (CNN) for model training. We establish a detection system for product packaging. The detection system provides a solution for automatic detection of package defection, which realizes rapid and accurate detection of product packaging.
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