arXiv:2606. 04345v1 Announce Type: cross Abstract: This paper presents HYolo, an intelligent IoT-based object detection framework that integrates hypergraph learning into the YOLO architecture.
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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.
arXiv:2409. 16808v3 Announce Type: replace-cross Abstract: Modern applications such as autonomous vehicles, intelligent surveillance, and smart city systems increasingly require object detection on resource-constrained edge devices.
arXiv:2607.17669v2 Announce Type: replace Abstract: Drone-based object detection technology has advanced rapidly, becoming increasingly sophisticated and efficient. Recently, research trends have exp...
arXiv:2602. 22794v2 Announce Type: replace Abstract: Internet of Things (IoT) networks face significant challenges such as limited communication bandwidth, constrained computational and energy resources, and highly dynamic wireless channel conditions.
JMT-YOLO is a new joint multi-task learning framework for domain adaptive object detection that builds on the Mean Teacher architecture. It adds an auxiliary semantic segmentation task and a masking mechanism to better model foreground–background boundaries and improve instance-level alignment. Experiments on Pascal VOC→Clipart1k, Cityscapes→Foggy Cityscapes, and KITTI→Cityscapes show that JMT‑YOLO outperforms existing state‑of‑the‑art methods, achieving 46.9%, 57.3%, and 61.0% detection performance respectively.
This paper provides a detailed overview of the Ultralytics YOLO family from YOLOv5 to YOLO27, highlighting key architectural changes, benchmarking results, and deployment considerations. It discusses the evolution of each version—YOLO27’s scale‑adaptive dual architecture, YOLO26’s loss and optimization refinements, YOLO11’s efficiency focus, YOLOv8’s anchor‑free detection, and YOLOv5’s modular ecosystem—alongside performance metrics on COCO and latency on TensorRT. The review also surveys applications in robotics, agriculture, surveillance, and manufacturing, and outlines future challenges such as dense scene handling, CNN‑Transformer integration, and hardware‑aware optimization.