用多视角图像提升橙子品质自动分级准确率
Orange Quality Grading with Deep Learning
- 采集单个橙子的多视角图像并合成一张全景图
- 使用ResNet-18和SqueezeNet模型实现三类分级，准确率更高
- 适合智能农业、自动化分选系统开发者参考
橙子分级是水果产业中的关键环节，用于根据大小、品质、成熟度和健康状况对橙子进行分类，确保食用安全，优化价格分配并提升客户满意度。自动化分级可实现更快处理速度、更高精度并减少人力成本。本文提出一种基于深度学习的机器视觉橙子分级方案。与通常仅从单一视角分析果实的方法不同，我们采集每个橙子的多视角图像，将其组合成一张拼合图，以实现更全面的果皮表征。随后，我们使用卷积神经网络（CNN）在拼合图像上训练，将橙子分为三类：优质、劣质和未定义。我们还对比了两种CNN模型（ResNet-18 和 SqueezeNet）的性能表现。实验表明，多视角分级优于单视角分级。
原文摘要 · Abstract (English)
Orange grading is a crucial step in the fruit industry, as it helps to sort oranges according to different criteria such as size, quality, ripeness, and health condition, ensuring safety for human consumption and better price allocation and client satisfaction. Automated grading enables faster processing, precision, and reduced human labor. In this paper, we implement a deep learning-based solution for orange grading via machine vision. Unlike typical grading systems that analyze fruits from a single view, we capture multiview images of each single orange in order to enable a richer representation. Afterwards, we compose the acquired images into one collage. This enables the analysis of the whole orange skin. We train a convolutional neural network (CNN) on the composed images to grade the oranges into three classes, namely good, bad, and undefined. We also evaluate the performance with two different CNNs (ResNet-18 and SqueezeNet). We show experimentally that multi-view grading is superior to single view grading.
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