Month: May 2020

Deep Learning for Classifying Food Waste

Amin Mazloumian
Hans-Joachim Gelke
Matthias Rosenthal

Institute of Embedded Systems Zurich University of Applied Sciences Zurich, Switzerland

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.

In this paper, a more informative view to food waste production behavior at the consumption stage is achieved through classifying food waste in waste bins. The classification task is feasible by processing images captured from food waste in the waste bins. The images are captured by installing cameras on top of the waste bins and monitoring the top surfaces of food waste in the bins. This study focuses on classifying food waste in half a million images captured by cameras installed on top of waste bins. The system design of a smart garbage systems that uses our classification is out of the scope of this study.

The automatic classification of food waste in waste bins is technically a difficult computer vision task for the following reasons.
a) It is visually hard to differentiate between edible and not-edible food waste. As an example consider distinguishing between eggs and empty eggshells.

b) Same food classes come in a wide variety of textures and colors if cooked or processed. c) Liquid food waste, e.g. soups and stews, and soft food waste, e.g. chopped vegetables and salads, can largely hide and cover visual features of other food classes.

In this research, we adopt a deep convolutional neural network approach for classifying food waste in waste bins. Deep convolutional neural networks are supervised machine learning algorithms that are able to perform complicated tasks on images, videos, sound, text, etc. The deep neural networks are composed of tens of convolutional layers (deep) that train on labelled data (supervised training) to learn target tasks. Labelled training data is composed of thousands of input- output pairs. In the training phase, the networks learn to produce the expected training output (labels) given the training input data. The training is performed by calculating millions of parameter values for feature extraction convolutional filters. In image processing, first layers of trained deep convolutional networks detect simple features, e.g. edges and corners. Based on the low level features extracted in first layers, deeper layers detect higher level features such as contours and shapes.

For more information please read our paper:
Deep Learning for Classifying Food Waste

Artificial Intelligence on Microcontrollers

Using artificial intelligence algorithms, specifically neural networks on microcontrollers offers several possibilities but reveals challenges: limited memory, low computing power and no operating system. In addition, an efficient workflow to port neural networks algorithms to microcontrollers is required. Currently, several frameworks that can be used to port neural networks to microcontrollers are available. We evaluated and compared four of them:

The frameworks differ considerably in terms of workflow, features and performance. Depending on the application, one has to select the best suited framework. On our github page we offer guides and example applications which can help you to get started with those frameworks!

The neural networks that are generated with all those frameworks are static. This means that once they are integrated into the firmware they cant be changed anymore. However, it would be beneficial if the neural network running on the microcontroller could adapt itself to a changing domain. We developed an algorithm (emb-adta) which could be used for unsupervised domain adaptation on microcontrollers. The prototype python implementation is also available on github!