Open Access

ARTICLE

Remote Sensing Imagery for Multi-Stage Vehicle Detection and Classification via YOLOv9 and Deep Learner

Naif Al Mudawi^1,*, Muhammad Hanzla², Abdulwahab Alazeb¹, Mohammed Alshehri¹, Haifa F. Alhasson³, Dina Abdulaziz AlHammadi⁴, Ahmad Jalal^2,5

1 Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, 55461, Saudi Arabia
2 Department of Computer Science, Air University, Islamabad, 44000, Pakistan
3 Department of Information Technology, College of Computer, Qassim University, Buraydah, 52571, Saudi Arabia
4 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
5 Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, 02841, Republic of Korea

* Corresponding Author: Naif Al Mudawi. Email:

(This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)

Computers, Materials & Continua 2025, 84(3), 4491-4509. https://doi.org/10.32604/cmc.2025.065490

Received 14 March 2025; Accepted 27 May 2025; Issue published 30 July 2025

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly employed in traffic surveillance, urban planning, and infrastructure monitoring due to their cost-effectiveness, flexibility, and high-resolution imaging. However, vehicle detection and classification in aerial imagery remain challenging due to scale variations from fluctuating UAV altitudes, frequent occlusions in dense traffic, and environmental noise, such as shadows and lighting inconsistencies. Traditional methods, including sliding-window searches and shallow learning techniques, struggle with computational inefficiency and robustness under dynamic conditions. To address these limitations, this study proposes a six-stage hierarchical framework integrating radiometric calibration, deep learning, and classical feature engineering. The workflow begins with radiometric calibration to normalize pixel intensities and mitigate sensor noise, followed by Conditional Random Field (CRF) segmentation to isolate vehicles. YOLOv9, equipped with a bi-directional feature pyramid network (BiFPN), ensures precise multi-scale object detection. Hybrid feature extraction employs Maximally Stable Extremal Regions (MSER) for stable contour detection, Binary Robust Independent Elementary Features (BRIEF) for texture encoding, and Affine-SIFT (ASIFT) for viewpoint invariance. Quadratic Discriminant Analysis (QDA) enhances feature discrimination, while a Probabilistic Neural Network (PNN) performs Bayesian probability-based classification. Tested on the Roundabout Aerial Imagery (15,474 images, 985K instances) and AU-AIR (32,823 instances, 7 classes) datasets, the model achieves state-of-the-art accuracy of 95.54% and 94.14%, respectively. Its superior performance in detecting small-scale vehicles and resolving occlusions highlights its potential for intelligent traffic systems. Future work will extend testing to nighttime and adverse weather conditions while optimizing real-time UAV inference.

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Keywords

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