用多输入结构融合彩色与轮廓图，实现苹果芒果缺陷检测100%准确
Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models
- 设计多输入架构，同时使用彩色图和轮廓图作为输入
- 基于MobileNetV2模型在测试集上达到100%分类准确率
- 适合农业质量检测、智能分拣等实际应用场景
本研究探讨了多输入架构在苹果和芒果健康与缺陷状态分类中的应用，结合RGB图像与轮廓图提升CNN模型性能。方法包括图像采集、数据预处理、模型训练与评估，采用MobileNetV2和VGG16两种CNN模型。结果表明，相较于仅使用RGB图像，加入轮廓图的多输入架构显著提升分类效果。其中以MobileNetV2模型表现最佳，实现100%准确率。该方法在水果外部质量检测中具有重要应用价值。
原文摘要 · Abstract (English)
This study presents an investigation into the utilization of a Multi-Input architecture for the classification of fruits (apples and mangoes) into healthy and defective states, employing both RGB and silhouette images. The primary aim is to enhance the accuracy of CNN models. The methodology encompasses image acquisition, preprocessing of datasets, training, and evaluation of two CNN models: MobileNetV2 and VGG16. Results reveal that the inclusion of silhouette images alongside the Multi-Input architecture yields models with superior performance compared to using only RGB images for fruit classification, whether healthy or defective. Specifically, optimal results were achieved using the MobileNetV2 model, achieving 100\% accuracy. This finding suggests the efficacy of this combined methodology in improving the precise classification of healthy or defective fruits, which could have significant implications for applications related to external quality inspection of fruits.
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