DGNN-YOLO: Interpretable Dynamic Graph Neural Networks with YOLO11 for Detecting and Tracking Small Occluded Objects in Urban Traffic

• URL: http://arxiv.org/abs/2411.17251v5
• Date: Thu, 09 Jan 2025 12:28:55 GMT
• Title: DGNN-YOLO: Interpretable Dynamic Graph Neural Networks with YOLO11 for Detecting and Tracking Small Occluded Objects in Urban Traffic
• Authors: Shahriar Soudeep, M. F. Mridha, Md Abrar Jahin, Nilanjan Dey,
• Abstract summary: This paper introduces DGNN-YOLO, a novel framework that integrates dynamic graph neural networks (DGNNs) with YOLO11 to address limitations. Unlike standard GNNs, DGNNs are chosen for their superior ability to dynamically update graph structures in real-time. This framework constructs and regularly updates its graph representations, capturing objects as nodes and their interactions as edges.
• Score: 2.0681376988193843
• License:
• Abstract: The detection and tracking of small, occluded objects such as pedestrians, cyclists, and motorbikes pose significant challenges for traffic surveillance systems because of their erratic movement, frequent occlusion, and poor visibility in dynamic urban environments. Traditional methods like YOLO11, while proficient in spatial feature extraction for precise detection, often struggle with these small and dynamically moving objects, particularly in handling real-time data updates and resource efficiency. This paper introduces DGNN-YOLO, a novel framework that integrates dynamic graph neural networks (DGNNs) with YOLO11 to address these limitations. Unlike standard GNNs, DGNNs are chosen for their superior ability to dynamically update graph structures in real-time, which enables adaptive and robust tracking of objects in highly variable urban traffic scenarios. This framework constructs and regularly updates its graph representations, capturing objects as nodes and their interactions as edges, thus effectively responding to rapidly changing conditions. Additionally, DGNN-YOLO incorporates Grad-CAM, Grad-CAM++, and Eigen-CAM visualization techniques to enhance interpretability and foster trust, offering insights into the model's decision-making process. Extensive experiments validate the framework's performance, achieving a precision of 0.8382, recall of 0.6875, and mAP@0.5:0.95 of 0.6476, significantly outperforming existing methods. This study offers a scalable and interpretable solution for real-time traffic surveillance and significantly advances intelligent transportation systems' capabilities by addressing the critical challenge of detecting and tracking small, occluded objects.

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