Accurate detection and tracking of small objects such as pedestrians, cyclists, and motorbikes are critical for traffic surveillance systems, which are crucial in improving road safety and decision-making in intelligent transportation systems. However, traditional methods struggle with challenges such as occlusion, low resolution, and dynamic traffic conditions, necessitating innovative approaches to address these limitations. This paper introduces DGNN-YOLO, a novel framework integrating dynamic graph neural networks (DGNN) with YOLO11 to enhance small object detection and tracking in traffic surveillance systems. The framework leverages YOLO11's advanced spatial feature extraction capabilities for precise object detection and incorporates DGNN to model spatial-temporal relationships for robust real-time tracking dynamically. By constructing and updating graph structures, DGNN-YOLO effectively represents objects as nodes and their interactions as edges, ensuring adaptive and accurate tracking in complex and dynamic environments. Extensive experiments demonstrate that DGNN-YOLO consistently outperforms state-of-the-art methods in detecting and tracking small objects under diverse traffic conditions, achieving the highest precision (0.8382), recall (0.6875), and mAP@0.5:0.95 (0.6476), showcasing its robustness and scalability, particularly in challenging scenarios involving small and occluded objects. This work provides a scalable, real-time traffic surveillance and analysis solution, significantly contributing to intelligent transportation systems.

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