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This paper presents a hybrid deep learning framework for fine-grained vehicle detection and classification, addressing challenges in urban traffic scenarios. The proposed methodology integrates Gaussian filtering, DeepLab for semantic segmentation, and DETR for object detection, achieving 89.5% classification accuracy and 88.2% detection accuracy on the FGVD dataset. The framework aims to enhance intelligent transportation systems by improving traffic monitoring and congestion management in complex urban en

Author
meshatayyab88
Language
EN