用真实与合成图像训练多输入CNN，提升水果畸形分类准确率。
Fruit Deformity Classification through Single-Input and Multi-Input Architectures based on CNN Models using Real and Synthetic Images
- 融合RGB图像与轮廓图的多输入架构提升识别能力。
- 苹果、芒果、草莓分类准确率分别达90%、94%、92%。
- 适合农业质检、智能分选系统研发人员参考。
本研究聚焦于在检测苹果、芒果和草莓外部品质过程中，通过基于卷积神经网络（CNN）的单输入与多输入架构，利用真实与合成图像识别果实畸形程度。数据集采用分割任意模型（SAM）进行分割，获取果实轮廓。单输入架构仅使用真实图像评估CNN模型，但提出一种利用合成图像预训练模型的方法以提升性能。多输入架构则同时输入RGB图像与果实轮廓，测试VGG16、MobileNetV2和CIDIS等模型。结果显示，采用MobileNetV2的多输入架构在识别果实畸形方面最有效，苹果、芒果和草莓的分类准确率分别为90%、94%和92%。结论表明，结合轮廓信息的多输入架构配合MobileNetV2模型，是水果畸形分类中最准确的方法。
原文摘要 · Abstract (English)
The present study focuses on detecting the degree of deformity in fruits such as apples, mangoes, and strawberries during the process of inspecting their external quality, employing Single-Input and Multi-Input architectures based on convolutional neural network (CNN) models using sets of real and synthetic images. The datasets are segmented using the Segment Anything Model (SAM), which provides the silhouette of the fruits. Regarding the single-input architecture, the evaluation of the CNN models is performed only with real images, but a methodology is proposed to improve these results using a pre-trained model with synthetic images. In the Multi-Input architecture, branches with RGB images and fruit silhouettes are implemented as inputs for evaluating CNN models such as VGG16, MobileNetV2, and CIDIS. However, the results revealed that the Multi-Input architecture with the MobileNetV2 model was the most effective in identifying deformities in the fruits, achieving accuracies of 90\%, 94\%, and 92\% for apples, mangoes, and strawberries, respectively. In conclusion, the Multi-Input architecture with the MobileNetV2 model is the most accurate for classifying levels of deformity in fruits.
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