"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 specific field of computer vision known as weather classification is still evolving and worthy proposals are still being made to improve the baseline. An opportunity to advance the field even further lies in the adoption of new techniques and tools that have been used to improve other fields. To achieve this, it is necessary to recognise that weather is a phenomenon that is a complex phenomena where semantic labelling of images can be a challenging task. A single image can encompass multiple weathers for instance partly cloudy. To exploit all the information that can be extracted from a single outdoor image, considering uncertainty as an opportunity rather than a problem is an important step." To this end, researchers from the University of Essex and the University of Birmingham tested different methods for classifying images of weather conditions: "The categories of interest for this contribution are sunny, cloudy, foggy, rainy and snowy images."
"The pipeline used for the work described in this paper is based on the work by Kalliatakis et al... There are two possible paths in the pipeline, either using superpixel masks or using the raw images directly. In the first mode of operation, the images are passed through a module that calculates the superpixels, generates the mask in a colour and applies it over the image... The images, enhanced with supeprixel mask if used, are divided into four partitions corresponding to positive training, positive testing, negative training, and negative testing. Then, the matdeeprep function, which uses pre-trained Convolutional Neural Network models, is used to extract deep features from the images in all the partitions. Finally, the features are used to train and test a binary Support Vector Machine classifier."
"For the sunny and cloudy categories, Lu et al. [13] developed a dataset as a part of their work. This dataset contains images from other datasets and images available online and labelled as part of their contribution. This dataset was made available on the web page associated with their paper. The images for the other categories are those belonging to RFS weather dataset. Since RFS weather Dataset contains 1100 images for each of its categories, 1100 images were taken from the categories in Lu et al. dataset, having a total of 5500 images distributed in five categories. To create a partition of the images for the Support Vector Machine classifier training and testing process, 70% of the images are used for the training phase, while 30% is used for the testing phase."
The overall best classification was the ResNet 50 architecture for all of the settings of the superpixel masks. The three variations of the residual networks (ResNet) used in the experimentation were among the four top performing models in the four different settings. These results are a good indication that the optimisation achieved by the inclusion of residual methods learning with shortcut connections has a positive effect in the overall task of weather classification, and it can benefit slightly from the use of superpixel masks as data augmentation. Residual Networks are thus not only successful in arbitrary image recognition, but can also work well as a solution to weather classification problem.