A New Machine Learning Algorithm for Weather Visibility and Food Recognition

DOI

10.2991/jrnal.k.190531.003How to use a DOI?

Keywords

Atmospheric visibility; convolutional neural networks; CCTV; graphic user interface; 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.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Young Im Cho
AU  - Akmaljon Palvanov
PY  - 2019
DA  - 2019/06/25
TI  - A New Machine Learning Algorithm for Weather Visibility and Food Recognition
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 12
EP  - 17
VL  - 6
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.190531.003
DO  - 10.2991/jrnal.k.190531.003
ID  - Cho2019
ER  -