Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks

TL;DR

This paper introduces a new multi-class weather dataset, proposes a data augmentation method using super pixel masks, and evaluates CNN architectures for weather classification to improve transportation safety.

Contribution

The paper presents a novel open-source dataset for three weather conditions and a super pixel-based data augmentation technique for CNN-based weather classification.

Findings

Effective CNN performance on the new dataset

Super pixel augmentation improves classification accuracy

Comprehensive evaluation of multiple CNN architectures

Abstract

Weather conditions often disrupt the proper functioning of transportation systems. Present systems either deploy an array of sensors or use an in-vehicle camera to predict weather conditions. These solutions have resulted in incremental cost and limited scope. To ensure smooth operation of all transportation services in all-weather conditions, a reliable detection system is necessary to classify weather in wild. The challenges involved in solving this problem is that weather conditions are diverse in nature and there is an absence of discriminate features among various weather conditions. The existing works to solve this problem have been scene specific and have targeted classification of two categories of weather. In this paper, we have created a new open source dataset consisting of images depicting three classes of weather i.e rain, snow and fog called RFS Dataset. A novel algorithm has also been proposed which has used super pixel delimiting masks as a form of data augmentation, leading to reasonable results with respect to ten Convolutional Neural Network architectures.