Authors - Tiurida Lily Anita, Dino Gustaf Leonandri, Mohd. Nor Shahizan Ali Abstract - In this paper, we address the problem of rainy condition classification in order to allow autonomous systems to ensure safe operation in different weather conditions of rain, especially for drones. The earlier weather condition classification methods are inclined towards using big and computationally costly models and cannot thus be employed in real-time on resource-constrained platforms such as drones and edge devices. The motivation behind this work is to introduce a light-weight, efficient deep model which would be able to classify various rain conditions with low computational cost so that it may be deployed efficiently on low-resource devices. We present a novel CNN architecture and evaluate its performance on a collection of seven distinct rain conditions. The models are bench marked against some of the state-of-the-art pretrained models to demonstrate the compromise between efficiency and accuracy. Performance is evaluated using accuracy, inference time, and model size. The model has accuracy 95.93% with least model size 89.09 KB with inference time of 32.664 ms bridging the gap in lightweight and real-time classification.
Open Zoom