Hurricane-Faster R-CNN-JS: Hurricane detection with faster R-CNN using artificial Jellyfish Search (JS) optimizer

Abstract

A hurricane is a type of storm called tropical cyclone (TC) and is likely to lead to severe storms and heavy rains. An early detection of hurricanes using satellite images can alarm people about upcoming disasters and thus minimize any casualties and material losses. Faster R-CNN is one of the most popular and recent object detection approaches. In the present study, AlexNet hyperparameters, which is a CNN model used as a feature extractor in Faster R-CNN, were optimized using artificial Jellyfish Search (JS), which is a recent algorithm, in order to propose a Faster R-CNN with a higher performance. The proposed approach is called Hurricane-Faster R-CNN-JS, since it is used as an early hurricane detection approach on satellite images before these hurricanes reach the land. The results of the present study demonstrated that hyperparameter optimization increased the detection performance of the proposed approach by 10% compared to AlexNet without optimized hyperparameters. As feature extractors of Faster R-CNN, the present study benefited from various architectures such as MobileNet-V2, GoogLeNet, AlexNet, ResNet 18, ResNet 50, VGG-16 and VGG-19 without any optimized hyperparameters to compare them with the proposed approach. It was observed that Average Precision (AP) of Hurricane-Faster R-CNN-JS was 97.39%, which was a remarkably higher AP level compared to other approaches.