Lightweight Deep Learning Model for Airborne Object Classification

Airborne object detection and classification play an important role in applications like military surveillance, border security, and public safety. The ability to accurately distinguish between airplanes, helicopters, drones, and birds is essential for monitoring and responding to potential aerial threats in real-time. This study leverages the AOD 4 dataset, which contains a comprehensive collection of 22,516 images spanning diverse environmental conditions. After organizing the dataset into four distinct classes, a custom lightweight Convolutional Neural Network (CNN) was developed to classify airborne objects. The proposed lightweight CNN achieves a remarkable accuracy of 97.12% and is able to outperform state-of-the-art pre-trained models in terms of accuracy and model size. On top of it, the model is also targeted at the various processors to unleash its potential to run in a real-time environment. The proposed model infers in 2.9 milliseconds on intel core i7 processor, which is a fourfold improvement compared to the MobileNetv3Small model. The findings from this study contribute to advancing the field of airborne object classification and pave the way for more robust real-world implementations.