Towards Airborne Object Detection: A Deep Learning Analysis

TL;DR

This paper presents a deep learning model based on EfficientNetB4 for airborne object classification and threat-level prediction, demonstrating high accuracy and addressing data scarcity with a new dataset, advancing automated threat assessment systems.

Contribution

Introduces a dual-task EfficientNetB4-based model and the AODTA dataset for improved airborne object classification and threat prediction, filling data gaps and benchmarking against ResNet-50.

Findings

Achieved 96% accuracy in object classification

Attained 90% accuracy in threat-level prediction

Outperformed ResNet-50 baseline

Abstract

The rapid proliferation of airborne platforms, including commercial aircraft, drones, and UAVs, has intensified the need for real-time, automated threat assessment systems. Current approaches depend heavily on manual monitoring, resulting in limited scalability and operational inefficiencies. This work introduces a dual-task model based on EfficientNetB4 capable of performing airborne object classification and threat-level prediction simultaneously. To address the scarcity of clean, balanced training data, we constructed the AODTA Dataset by aggregating and refining multiple public sources. We benchmarked our approach on both the AVD Dataset and the newly developed AODTA Dataset and further compared performance against a ResNet-50 baseline, which consistently underperformed EfficientNetB4. Our EfficientNetB4 model achieved 96% accuracy in object classification and 90% accuracy in threat-level prediction, underscoring its promise for applications in surveillance, defense, and airspace management. Although the title references detection, this study focuses specifically on classification and threat-level inference using pre-localized airborne object images provided by existing datasets.