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: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.
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: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.
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:2607. 02981v1 Announce Type: cross Abstract: Recent advancements in the Internet of Things (IoT) emphasize the urgent need for advanced network security, as IoT networks feature dynamic topologies, imbalanced traffic, and complex attack patterns.
arXiv:2609.24455v1 Announce Type: cross Abstract: Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. Howeve...
The paper surveys how commonsense reasoning is being integrated into computer vision, moving beyond traditional CNNs that only detect objects. It reviews methods that use knowledge graphs, scene graphs, neuro-symbolic models, and transformers to add contextual understanding, thereby improving object recognition and spatial reasoning. The authors also discuss current limitations such as dataset bias and knowledge gaps, and propose future research directions in cross‑modal reasoning, scalable knowledge injection, and hybrid architectures.
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 conducts a systematic empirical study of multi‑object tracking (MOT) algorithms, focusing on how detection and association components affect overall performance. By evaluating state‑of‑the‑art methods on benchmarks such as MOT16/17/20, SportsMOT, DanceTrack, and CrowdTrack, the authors find that detection quality has a far greater impact than association strategies, and that transformer‑based end‑to‑end models are more robust to detection variations but computationally expensive. The study provides a unified pipeline diagram and practical guidance for researchers and practitioners in selecting and designing MOT systems.
The recent large video foundation model, SAM2, enables segment anything in both images and videos, serving as a powerful base model for various applications. However, many of such use cases require to operate on resource-constrained devices like mobile phones and laptops.
arXiv:2608.21937v1 Announce Type: new Abstract: Incremental Object Detection (IOD) aims to enable detectors to continuously learn novel categories while preserving previously acquired knowledge. Howe...
arXiv:2606. 05587v1 Announce Type: cross Abstract: Multi-object tracking (MOT) from UAV imagery presents unique challenges: altitude varies across sequences, objects are small and densely packed, and frequent occlusion causes identity switches.