Multi-Class Weather Classification From Single Images With Convolutional Neural Networks On Embedded Hardware

Abstract

The paper is focused on creating a lightweight machine learning solution for classificationof weather conditions from input images, that can process the input data in real time on embeddeddevices. The approach to the classification uses deep convolutional neural networks architecture withfocus on lightweight design and fast inference, while providing high accuracy results. The focus oncreating lightweight convolutional neural network architecture capable of classification of weatherconditions also enables usage of the network in real time applications at the edge.