Nighttime Intelligent UAV-Based Vehicle Detection and Classification Using YOLOv10 and Swin Transformer

Abstract

Unmanned Aerial Vehicles (UAVs) have become indispensable for intelligent traffic monitoring, particularly in low-light conditions, where traditional surveillance systems struggle. This study presents a novel deep learning-based framework for nighttime aerial vehicle detection and classification that addresses critical challenges of poor illumination, noise, and occlusions. Our pipeline integrates MSRCR enhancement with OPTICS segmentation to overcome low-light challenges, while YOLOv10 enables accurate vehicle localization. The framework employs GLOH and Dense-SIFT for discriminative feature extraction, optimized using the Whale Optimization Algorithm to enhance classification performance. A Swin Transformer-based classifier provides the final categorization, leveraging hierarchical attention mechanisms for robust performance. Extensive experimentation validates our approach, achieving detection mAP@0.5 scores of 91.5% (UAVDT) and 89.7% (VisDrone), alongside classification accuracies of 95.50% and 92.67%, respectively. These results outperform state-of-the-art methods by up to 5.10% in accuracy and 4.2% in mAP, demonstrating the framework’s effectiveness for real-time aerial surveillance and intelligent traffic management in challenging nighttime environments.