Real-time UAV object detection and task offloading: powered through edge YOlOv9-Lite and cloud reinforcement transformer learning in various scenarios

Real-time traffic congestion mapping and bottleneck analysis using an edge-deployable lightweight deep learning framework for UAV-assisted monitoring

UAVCoNet is presented, a lightweight deep learning architecture optimized for real-time traffic occupancy estimation, congestion heatmap generation, and bottleneck analysis and can be integrated with intelligent transportation system for rapid assessment of traffic state and subsequently for real time traffic managemen...

AerialReID-SORT: Dynamic Graph Framework for Secure and Intelligent UAV based Traffic Surveillance and Congestion Forecasting

An Edge-Deployable Lightweight UAV Detection and Net-Capture System Based on NCDet-YOLO

An edge-deployable UAV detection and net-capture system based on Net-Capture Detection YOLO (NCDet-YOLO), developed from YOLOv8n, which incorporates C2f_Faster, SPD_Conv, EMA, and a lightweight three-scale detection head, with CrossKD used to compensate for accuracy loss caused by structural compression.

LBF-YOLO: lightweight obstacle detection for UAVs in complex low-altitude environments

E’CHIT: Identity-Stable Operator-Centric UAV Tracking for Disaster Response

Search-and-rescue (SAR) missions following earthquakes and other disasters require aerial video perception systems that do more than detect objects in isolated frames. Operators must maintain the identities of access points, vehicles, responders, hazards, and other mission-relevant targets despite UAV ego-motion, dust,...

LiteUAV-Det: An Efficient Lightweight Deep Learning Framework for UAV-to-UAV Small Target Detection in Complex Aerial Scenes

LiteUAV-Det is proposed, a lightweight and real-time UAV-to-UAV small object detection framework designed to achieve an optimal balance between detection accuracy and computational efficiency and demonstrates competitive detection performance compared with state-of-the-art lightweight detectors.