@Article{cmc.2025.067895,
AUTHOR = {Ghulam Mujtaba, Wenbiao Liu, Mohammed Alshehri, Yahya AlQahtani, Nouf Abdullah Almujally, Hui Liu},
TITLE = {Aerial Images for Intelligent Vehicle Detection and Classification via YOLOv11 and Deep Learner},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {1},
PAGES = {1--19},
URL = {http://www.techscience.com/cmc/v86n1/64404},
ISSN = {1546-2226},
ABSTRACT = {As urban landscapes evolve and vehicular volumes soar, traditional traffic monitoring systems struggle to scale, often failing under the complexities of dense, dynamic, and occluded environments. This paper introduces a novel, unified deep learning framework for vehicle detection, tracking, counting, and classification in aerial imagery designed explicitly for modern smart city infrastructure demands. Our approach begins with adaptive histogram equalization to optimize aerial image clarity, followed by a cutting-edge scene parsing technique using Mask2Former, enabling robust segmentation even in visually congested settings. Vehicle detection leverages the latest YOLOv11 architecture, delivering superior accuracy in aerial contexts by addressing occlusion, scale variance, and fine-grained object differentiation. We incorporate the highly efficient ByteTrack algorithm for tracking, enabling seamless identity preservation across frames. Vehicle counting is achieved through an unsupervised DBSCAN-based method, ensuring adaptability to varying traffic densities. We further introduce a hybrid feature extraction module combining Convolutional Neural Networks (CNNs) with Zernike Moments, capturing both deep semantic and geometric signatures of vehicles. The final classification is powered by NASNet, a neural architecture search-optimized model, ensuring high accuracy across diverse vehicle types and orientations. Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance, achieving 96% detection, 94% tracking, and 96.4% classification accuracy. On the UAVDT dataset, the system attains 95% detection, 93% tracking, and 95% classification accuracy, confirming its robustness across diverse aerial traffic scenarios. These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability, making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.},
DOI = {10.32604/cmc.2025.067895}
}