RAIN GUARD: An Intelligent Rain Detection System
Bijusha1
*,
- 1Anna University Chennai, Assistant Professor, Loyola Institute of Technology and Science, Kanyakumari Dist., 629178, IN
References
References
[1] 1. Dosovitskiy, A., et al. (2021). "An
Image is Worth
16x16 Words: Transformers for Image
Recognition at Scale." International Conference on Learning Representations (ICLR).
[2] 2. He, K., et al. (2016). "Deep Residual Learning for Image Recognition." Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition.
[3] 3. Vaswani,
A., et al. (2017). "Attention is All You Need." Advances in Neural Information Processing
Systems (NeurIPS).
4.Radford, A., et al. (2021). "Learning Transferable Visual Models From Natural Language Supervision."
[1]
5.Howard,
A. G., et al. (2017). "MobileNets: Efficient Convolutional Neural Networks
for Mobile Vision Applications." arXiv preprint arXiv:1704.04861.
[2]
6.Onneberger,
O., Fischer, P., & Brox, T. (2015). "U-Net: Convolutional Networks for
Biomedical Image Segmentation." Medical
Image Computing and Computer-Assisted Intervention (MICCAI).
[3]
7.Lee, S., & Kim, D. (2015).
"Real-time Rainfall Detection
Using Ground-based Sky Image
Data
and Machine Learning Techniques." IEEE Sensors Journal.
Keywords: Rain Guard, Weather Prediction, Vision Transformer, Contextual weather Descriptions, Real -time alerts, Flask backend.
Citation: Bijusha*,Bijusha ( 2025), RAIN GUARD: An Intelligent Rain Detection System . , 13(3): 1-6
Received: 25/08/2025; Accepted: 27/08/2025;
Published: 27/08/2025
Edited by:
Mr.ERES JOURNALS