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.

翻译：准确检测与跟踪行人、骑行者及摩托车等小目标对于交通监控系统至关重要，该系统对提升道路安全及智能交通系统中的决策制定具有关键作用。然而，传统方法在处理遮挡、低分辨率及动态交通条件等挑战时存在困难，亟需创新方法以应对这些局限。本文提出DGNN-YOLO，一种将动态图神经网络与YOLO11相结合的新型框架，旨在增强交通监控系统中的小目标检测与跟踪能力。该框架利用YOLO11先进的空间特征提取能力实现精确目标检测，并引入DGNN动态建模时空关系以实现鲁棒的实时跟踪。通过构建并更新图结构，DGNN-YOLO将目标有效表示为节点，其交互关系表示为边，从而确保在复杂动态环境中实现自适应且精确的跟踪。大量实验表明，DGNN-YOLO在不同交通条件下检测与跟踪小目标时，其性能始终优于现有先进方法，取得了最高的精确率（0.8382）、召回率（0.6875）和mAP@0.5:0.95（0.6476），展现了其鲁棒性与可扩展性，尤其是在涉及小目标及遮挡目标的挑战性场景中。本工作提供了一种可扩展的实时交通监控与分析解决方案，为智能交通系统做出了重要贡献。