论文标题
对芒果的自动质量分级的深度学习：方法和见解
Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights
论文作者
论文摘要
芒果的质量分级对于芒果种植者来说是一项至关重要的任务，因为它极大地影响了他们的利润。但是，直到今天，这个过程仍然依赖于容易疲劳和错误的人类的费力努力。为了解决这个问题，本文使用各种卷积神经网络（CNN）处理了分级任务，这是计算机视觉中经过久经考验的深度学习技术。涉及的模型包括Mask R-CNN（用于背景拆除），ImageNet挑战的众多冠军，即Alexnet，VGGS和Resnets；而且，一个自我定义的卷积自动编码器分类器（Convae-CLF）的家族，其灵感来自分类任务中多任务学习的好处。这项工作还采用了传递学习，通过利用ImageNet预处理的重量。除了详细介绍预处理技术，培训细节以及由此产生的表现外，我们还迈出了一步，以提供可解释的见解，以借助显着图和主要成分分析（PCA）的帮助。这些见解为复杂的深度学习黑匣子提供了简洁，有意义的瞥见，促进了信任，也可以在现实世界中的用例中向人类展示，以审查分级结果。
The quality grading of mangoes is a crucial task for mango growers as it vastly affects their profit. However, until today, this process still relies on laborious efforts of humans, who are prone to fatigue and errors. To remedy this, the paper approaches the grading task with various convolutional neural networks (CNN), a tried-and-tested deep learning technology in computer vision. The models involved include Mask R-CNN (for background removal), the numerous past winners of the ImageNet challenge, namely AlexNet, VGGs, and ResNets; and, a family of self-defined convolutional autoencoder-classifiers (ConvAE-Clfs) inspired by the claimed benefit of multi-task learning in classification tasks. Transfer learning is also adopted in this work via utilizing the ImageNet pretrained weights. Besides elaborating on the preprocessing techniques, training details, and the resulting performance, we go one step further to provide explainable insights into the model's working with the help of saliency maps and principal component analysis (PCA). These insights provide a succinct, meaningful glimpse into the intricate deep learning black box, fostering trust, and can also be presented to humans in real-world use cases for reviewing the grading results.
