用超图学习增强YOLO，提升物联网场景下目标检测的上下文理解能力。
HYolo: An Intelligent IoT-Based Object Detection System Using Hypergraph Learning
- 将超图学习融入YOLO架构，捕捉多对象高阶关联关系。
- 在COCO数据集上mAP@50提升约12%，检测精度与鲁棒性显著增强。
- 适合需要上下文感知的智能物联网视觉系统部署。
本文提出HYolo，一种基于物联网的智能目标检测框架，将超图学习引入YOLO架构。传统YOLO模型主要捕获成对特征交互，难以建模物体与上下文特征间的复杂高阶关系。为解决此问题，HYolo引入超图学习以捕捉更丰富的上下文依赖，提升物体表征能力。在COCO数据集上的实验表明，该方法相比基线YOLO模型实现约12%的mAP@50提升，同时提高整体检测准确率与鲁棒性。通过建模高阶特征关系，HYolo增强了上下文理解能力，在物联网环境下的目标检测表现更可靠。结果表明，将超图学习整合至目标检测流程，为智能、上下文感知的物联网视觉系统提供了有前景的方向。
原文摘要 · Abstract (English)
This paper presents HYolo, an intelligent IoT-based object detection framework that integrates hypergraph learning into the YOLO architecture. Traditional YOLO-based object detection models primarily capture pairwise feature interactions and may fail to model complex high-order relationships among objects and contextual features. To address this limitation, HYolo incorporates hypergraph learning to capture richer contextual dependencies and improve object representation. Experimental evaluation on the COCO dataset demonstrates significant performance improvements over baseline YOLO models. The proposed approach achieves approximately 12% improvement in mAP@50 while enhancing overall detection accuracy and robustness. By modeling high-order feature relationships, HYolo provides improved contextual understanding and more reliable object detection performance in IoT-based environments. The results indicate that integrating hypergraph learning into object detection pipelines offers a promising direction for intelligent and context-aware IoT vision systems.
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