Ghulam Mujtaba^1,2,#, Wenbiao Liu^1,#, Mohammed Alshehri³, Yahya AlQahtani⁴, Nouf Abdullah Almujally⁵, Hui Liu^1,6,7,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.067895 - 10 November 2025

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… More >

Heng Wang¹, Jian-Hua Qin^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1037-1052, 2025, DOI:10.32604/cmc.2025.067650 - 29 August 2025

Abstract Vehicle detection is a crucial aspect of intelligent transportation systems (ITS) and autonomous driving technologies. The complexity and diversity of real-world road environments, coupled with traffic congestion, pose significant challenges to the accuracy and real-time performance of vehicle detection models. To address these challenges, this paper introduces a fast and accurate vehicle detection algorithm named BES-Net. Firstly, the BoTNet module is integrated into the backbone network to bolster the model’s long-distance dependency, address the complexities and diversity of road environments, and accelerate the detection speed of the BES-Net network. Secondly, to accommodate the varying sizes… More >

Syed Sajid Ullah^1,*, Muhammad Zunair Zamir², Ahsan Ishfaq², Salman Khan¹

Journal on Artificial Intelligence, Vol.7, pp. 255-274, 2025, DOI:10.32604/jai.2025.069008 - 29 August 2025

Abstract Accurate vehicle detection is essential for autonomous driving, traffic monitoring, and intelligent transportation systems. This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module, Convolutional Block Attention Module (CBAM), and Deformable Convolutional Networks v2 (DCNv2). The Ghost Module streamlines feature generation to reduce redundancy, CBAM applies channel and spatial attention to improve feature focus, and DCNv2 enables adaptability to geometric variations in vehicle shapes. These components work together to improve both accuracy and computational efficiency. Evaluated on the KITTI dataset, the proposed model achieves 95.4% mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% More >

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

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4677-4697, 2025, DOI:10.32604/cmc.2025.065899 - 30 July 2025

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 More >

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

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4491-4509, 2025, DOI:10.32604/cmc.2025.065490 - 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… More >

Pengfei Wang^1,2,3, Jiwu Sun², Lu Lu^1,4, Hongchen Li¹, Hongzhe Liu², Cheng Xu², Yongqiang Liu^1,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2883-2903, 2025, DOI:10.32604/cmc.2025.065267 - 03 July 2025

Abstract Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms. Traditional methods often struggle to address issues such as image blurring, dynamic noise interference, and variations in target scale. Conventional neural network (CNN)-based target detection approaches face notable limitations in such adverse weather scenarios, primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances. To address these challenges, this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network (DCN) enhanced with a multi-scale dilated attention (MSDA) mechanism. Specifically,… More >

Yahia Said^1,*, Yahya Alassaf², Taoufik Saidani³, Refka Ghodhbani³, Olfa Ben Rhaiem⁴, Ali Ahmad Alalawi¹

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.058903 - 19 December 2024

Abstract The integration of Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITS) holds transformative potential for real-time traffic monitoring, a critical component of emerging smart city infrastructure. UAVs offer unique advantages over stationary traffic cameras, including greater flexibility in monitoring large and dynamic urban areas. However, detecting small, densely packed vehicles in UAV imagery remains a significant challenge due to occlusion, variations in lighting, and the complexity of urban landscapes. Conventional models often struggle with these issues, leading to inaccurate detections and reduced performance in practical applications. To address these challenges, this paper introduces CFEMNet,… More >

Wenming Wang^1,2,3,*, Zhiquan Liu¹, Shumin Zhang¹, Guijiang Liu¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2517-2545, 2024, DOI:10.32604/cmc.2024.055184 - 18 November 2024

Abstract Advancements in the vehicular network technology enable real-time interconnection, data sharing, and intelligent cooperative driving among vehicles. However, malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle users. Trust mechanisms serve as an effective solution to this issue. In recent years, many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes, incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the area. In this paper, we propose a distributed vehicular network scheme based on trust scores. Specifically, the designed architecture partitions multiple More >

Naeem Raza¹, Muhammad Asif Habib¹, Mudassar Ahmad¹, Qaisar Abbas^2,*, Mutlaq B. Aldajani², Muhammad Ahsan Latif³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 911-931, 2024, DOI:10.32604/cmc.2024.055049 - 15 October 2024

Abstract Vision-based vehicle detection in adverse weather conditions such as fog, haze, and mist is a challenging research area in the fields of autonomous vehicles, collision avoidance, and Internet of Things (IoT)-enabled edge/fog computing traffic surveillance and monitoring systems. Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time. To evaluate vision-based vehicle detection performance in foggy weather conditions, state-of-the-art Vehicle Detection in Adverse Weather Nature (DAWN) and Foggy Driving (FD) datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle… More >

Yunxiang Liu, Haili Ma, Jianlin Zhu^*, Qiangbo Zhang

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2963-2978, 2024, DOI:10.32604/cmc.2024.053710 - 15 August 2024

Abstract To enhance the efficiency and accuracy of environmental perception for autonomous vehicles, we propose GDMNet, a unified multi-task perception network for autonomous driving, capable of performing drivable area segmentation, lane detection, and traffic object detection. Firstly, in the encoding stage, features are extracted, and Generalized Efficient Layer Aggregation Network (GELAN) is utilized to enhance feature extraction and gradient flow. Secondly, in the decoding stage, specialized detection heads are designed; the drivable area segmentation head employs DySample to expand feature maps, the lane detection head merges early-stage features and processes the output through the Focal Modulation More >