@article{Alnusayri2026, 
author = {Mohammed Alnusayri and Ghulam Mujtaba and Nouf Abdullah Almujally and Shuoa S. Aitarbi and Asaad Algarni and Ahmad Jalal and Jeongmin Park},
title = {Traffic Vision: UAV-Based Vehicle Detection and Traffic Pattern Analysis via Deep Learning Classifier},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {3},
pages = {7},
keywords = {Smart traffic system, drone devices, machine learner, dynamic complex scenes, VGG-16 classifier},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.071804},
doi = {10.32604/cmc.2025.071804},
abstract = {This paper presents a unified Unmanned Aerial Vehicle-based (UAV-based) traffic monitoring framework that integrates vehicle detection, tracking, counting, motion prediction, and classification in a modular and co-optimized pipeline. Unlike prior works that address these tasks in isolation, our approach combines You Only Look Once (YOLO) v10 detection, ByteTrack tracking, optical-flow density estimation, Long Short-Term Memory-based (LSTM-based) trajectory forecasting, and hybrid Speeded-Up Robust Feature (SURF) + Gray-Level Co-occurrence Matrix (GLCM) feature engineering with VGG16 classification. Upon the validation across datasets (UAVDT and UAVID) our framework achieved a detection accuracy of 94.2%, and 92.3% detection accuracy when conducting a real-time UAV field validation. Our comprehensive evaluations, including multi-metric analyses, ablation studies, and cross-dataset validations, confirm the framework’s accuracy, efficiency, and generalizability. These results highlight the novelty of integrating complementary methods into a single framework, offering a practical solution for accurate and efficient UAV-based traffic monitoring.}
}