Paper Info
Title
Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks.
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.
Year
Venue
Keywords
2018
NASA/ESA Conference on Adaptive Hardware and Systems
Weather Classification,Convolutional Neural Network,Superpixels,Data Augmentation
DocType
Volume
ISSN
Conference
abs/1808.00588
1939-7003
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Jose Carlos Villarreal Guerra100.34
Zeba Khanam200.34
Shoaib Ehsan311024.43
Rustam Stolkin452739.74
Klaus D. McDonald-Maier532754.43