MobileNetV2 based deep neural network for automated rain condition detection in UAV imagery

Document Type : Original Article

Authors

Department of Mathematics, SAS, Vellore Institute of Technology, Vellore

Abstract
Unmanned Aerial Vehicles (UAVs) are used rapidly in different fields. Few of the important areas where UAVs are essential in disaster management and agriculture owing to their cost-effectiveness and accessibility to remote areas. However, adverse weather conditions like rain hinder their navigation. This study is a deep learning approach using MobileNetV2 to detect rainy conditions from UAV captured images. It aims to enhance the operational safety and efficiency. The balanced dataset of 245K
images across seven rain classes was used. The dataset was divided into training, testing, and validation sets in the ratio 70:15:15. This convolutional neural network model achieved a test accuracy of 95.35%. This suggests that the model is reliable and robust and can be further researched for real-time deployment.

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Volume 6, Issue 4
Autumn 2025
Pages 74-80

  • Receive Date 20 October 2025
  • Revise Date 15 November 2025
  • Accept Date 21 November 2025