@Article{cmc.2024.058903,
AUTHOR = {Yahia Said, Yahya Alassaf, Taoufik Saidani, Refka Ghodhbani, Olfa Ben Rhaiem, Ali Ahmad Alalawi},
TITLE = {Context-Aware Feature Extraction Network for High-Precision UAV-Based Vehicle Detection in Urban Environments},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {81},
YEAR = {2024},
NUMBER = {3},
PAGES = {4349--4370},
URL = {http://www.techscience.com/cmc/v81n3/59066},
ISSN = {1546-2226},
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, an advanced deep learning model specifically designed for high-precision vehicle detection in complex urban environments. CFEMNet is built on the High-Resolution Network (HRNet) architecture and integrates a Context-aware Feature Extraction Module (CFEM), which combines multi-scale feature learning with a novel Self-Attention and Convolution layer setup within a Multi-scale Feature Block (MFB). This combination allows CFEMNet to accurately capture fine-grained details across varying scales, crucial for detecting small or partially occluded vehicles. Furthermore, the model incorporates an Equivalent Feed-Forward Network (EFFN) Block to ensure robust extraction of both spatial and semantic features, enhancing its ability to distinguish vehicles from similar objects. To optimize computational efficiency, CFEMNet employs a local window adaptation of Multi-head Self-Attention (MSA), which reduces memory overhead without sacrificing detection accuracy. Extensive experimental evaluations on the UAVDT and VisDrone-DET2018 datasets confirm CFEMNet’s superior performance in vehicle detection compared to existing models. This new architecture establishes CFEMNet as a benchmark for UAV-enabled traffic management, offering enhanced precision, reduced computational demands, and scalability for deployment in smart city applications. The advancements presented in CFEMNet contribute significantly to the evolution of smart city technologies, providing a foundation for intelligent and responsive traffic management systems that can adapt to the dynamic demands of urban environments.},
DOI = {10.32604/cmc.2024.058903}
}