TinyEmergencyNet: A hardware-friendly ultra-lightweight deep learning model
                  for aerial scene image classification

Obed M. Mogaka; Rami Zewail; Koji Inoue; Mohammed S. Sayed

doi:10.1007/s11554-024-01430-y

DOI

Abstract

We propose TinyEmergencyNet, an ultra-lightweight convolutional neural network designed for real-time aerial scene classification on resource-constrained hardware. The architecture achieves competitive accuracy with a fraction of the parameters and multiply-accumulate operations of standard models, making it suitable for deployment on embedded and FPGA platforms.

Citation

@article{mogaka2024tiny,
  author = {Mogaka, Obed M. and Zewail, Rami and Inoue, Koji and Sayed, Mohammed S.},
  title = {{TinyEmergencyNet}: A hardware-friendly ultra-lightweight deep learning model
                  for aerial scene image classification},
  journal = {Journal of Real-Time Image Processing},
  volume = {21},
  number = {2},
  pages = {51},
  year = {2024},
  doi = {10.1007/s11554-024-01430-y},
  keywords = {deep learning, aerial scene classification, lightweight CNN, FPGA, embedded ML}
}