论文标题
深入学习食物浪费
Deep Learning for Classifying Food Waste
论文作者
论文摘要
每年，世界上生产的食物中有三分之一（约13亿吨）每年都会丢失或浪费。通过对个人消费者的食物浪费进行分类并提高对措施的认识，可以大大减少可避免的食物浪费。在这项研究中，我们使用深度学习将食物垃圾分类为一百万张由安装在食物垃圾箱顶部的相机捕获的图像。我们专门设计了一个深层神经网络，该网络将食物浪费在废物垃圾箱中扔掉时，对食物浪费进行了分类。我们的方法介绍了如何对深度学习网络进行定制，以最好地从可用的培训数据中学习。
One third of food produced in the world for human consumption -- approximately 1.3 billion tons -- is lost or wasted every year. By classifying food waste of individual consumers and raising awareness of the measures, avoidable food waste can be significantly reduced. In this research, we use deep learning to classify food waste in half a million images captured by cameras installed on top of food waste bins. We specifically designed a deep neural network that classifies food waste for every time food waste is thrown in the waste bins. Our method presents how deep learning networks can be tailored to best learn from available training data.
