Hai Liao

TL;DR: Wang et al. as mentioned in this paper proposed a new date dimension to calculate the number of APs together with the time dimension, and provided new insights to set up thresholds of online APs automatically.

TL;DR: The suggested LIDA‐YOLO model requires fewer samples and presents a stronger generalization ability than previous works, and achieves object detection in low‐illumination images through an unsupervised learning strategy.

Abstract: Drone-view object detection (DroneDet) is a computer vision method for locating objects and predicting their categories in aerial images. Popular methods are often plagued by high energy consumption and severe noise interference in dynamic aerial environments. In this study, we propose a novel integer-valued spiking neural network (SNN)-based approach called SNN-Drone to optimize both energy consumption and performance. Regarding high energy consumption, a new integer-valued encoding (IVE) scheme transforms YOLOv8 features into high-fidelity integer spike trains. The IVE effectively reduces the high energy consumption associated with conventional binary spike encoding. For noise interference, our dual-branch SNN block (DBSB) enhances feature representation by separating spatial and semantic processing, while the normalized Wasserstein distance (NWD) loss refines localization accuracy for small and densely packed targets. These modules significantly improve detection performance. In addition, we construct UAVDT-SNN, the first large-scale neuromorphic dataset for uncrewed aerial vehicle (UAV) object detection with temporally encoded motion and diverse aerial scenes. Extensive experiments on the VisDrone 2019, UAVDT, and UAVDT-SNN datasets demonstrate that SNN-Drone achieves state-of-the-art performance, outperforming both baseline artificial neural networks (ANNs) and advanced YOLOv8–v12 variants. Notably, SNN-Drone-s improves the energy efficiency by $7.7\times $ compared to YOLOv8s. This work is the first to show that SNNs can serve as an energy-efficient yet competitive solution for drone-view object detection, narrowing the performance–energy gap with ANNs. Our code is available at https://github.com/iliaohai/SNN-Drone

TL;DR: A decision-making algorithm based on vote trees of random forest is proposed for early abdominal aortic aneurysm (AAA) diagnosis, achieving higher accuracy and trustworthy results, enabling timely intervention and reducing mortality.