A New Machine Learning Algorithm for Weather Visibility and Food Recognition

Abstract

Due to the recent improvement in computer performance and computational tools, deep convolutional neural networks (CNNs) have been established as powerful class of models in various problems such as image classification, recognition, and object detection. In this study, we address two fundamentally dissimilar classification tasks: (i) visibility estimation and (ii) food recognition on a basis of CNNs. For each task, we propose two different data-driven approaches focusing on to reduce computation time and cost. Both models use camera imagery as inputs and works in real-time. The first proposed method is designed to estimate visibility using our new collected dataset, which consist of Closed-circuit Television (CCTV) camera images captured in various weather conditions, especially in dense fog and low-cloud. Unlikely, the second model designed to recognize dishes using artificially generated images. We collected a limited number of images from the web and artificially extended the dataset using data augmentation techniques for boosting the performance of the model. Both purposing models show high classification accuracy, requiring less computation power and time. This paper describes the complexity of both tasks and also other essential details.