用真实与合成数据训练简单CNN，实现91.7%准确率的香蕉成熟度分类
Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets
- 融合真实与合成数据构建鲁棒数据集，解决样本不足问题
- 采用迁移学习优化简单CNN，达到91.7%分类准确率
- 模型轻量高效，适合工业级实时成熟度检测
香蕉成熟度是决定其品质的关键指标，国际营销标准对此有明确要求。然而，目前工业场景中仍依赖人工评估。虽然卷积神经网络（CNN）在该任务中展现出潜力，但受限于高质量训练数据的缺乏。为此，本文构建了一个结合真实与合成数据的鲁棒数据集，提出一种简化CNN架构，通过合成数据预训练并利用迁移学习优化，实现对真实图像的成熟度分类。模型在多种架构、超参数配置和优化器下进行评估，最终在测试集上达到0.917的准确率，同时具备快速执行能力。
原文摘要 · Abstract (English)
The level of ripeness is essential in determining the quality of bananas. To correctly estimate banana maturity, the metrics of international marketing standards need to be considered. However, the process of assessing the maturity of bananas at an industrial level is still carried out using manual methods. The use of CNN models is an attractive tool to solve the problem, but there is a limitation regarding the availability of sufficient data to train these models reliably. On the other hand, in the state-of-the-art, existing CNN models and the available data have reported that the accuracy results are acceptable in identifying banana maturity. For this reason, this work presents the generation of a robust dataset that combines real and synthetic data for different levels of banana ripeness. In addition, it proposes a simple CNN architecture, which is trained with synthetic data and using the transfer learning technique, the model is improved to classify real data, managing to determine the level of maturity of the banana. The proposed CNN model is evaluated with several architectures, then hyper-parameter configurations are varied, and optimizers are used. The results show that the proposed CNN model reaches a high accuracy of 0.917 and a fast execution time.
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