Nighttime Intelligent UAV-Based Vehicle Detection and Classification Using YOLOv10 and Swin Transformer
Abdulwahab Alazeb, Muhammad Hanzla, Naif Al Mudawi, Mohammed Alshehri, Haifa F. Alhasson, Dina Abdulaziz AlHammadi, Ahmad Jalal
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 [email protected] 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.