HYolo: An Intelligent IoT-Based Object Detection System Using Hypergraph Learning
2026-06-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors created HYolo, a new system that helps smart devices detect objects better by using a special math tool called hypergraph learning combined with an existing method called YOLO. Normal YOLO only looks at simple connections between objects, but HYolo can understand more complex relationships and context. Testing on a popular image dataset showed that HYolo is about 12% better at spotting objects correctly. This means the authors improved object detection for smart devices by making the system smarter about how things relate to each other.
YOLOhypergraph learningobject detectioncontextual featuresIoTCOCO datasetmean Average Precision (mAP)feature interactions
Authors
Isha Abid, Fawad Khan, Muhammad Khuram Shahzad
Abstract
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.