TL;DR
ATU-Net is proposed, a YOLO11-based detector that integrates an enhanced multi-scale aggregation backbone, a shuffle-fusion cross stage partial neck module, a feature-sharing detection head, and Focaler-ShapeIoU, showing that the accuracy gain is retained with real-time embedded inference despite additional runtime overhead.
A Lightweight Feature-Fusion and Small-Target Enhancement Network (LFE-YOLO), a lightweight detector that coordinates partial-channel feature extraction, efficient cross-scale fusion, high-resolution prediction, background-interference suppression, and stable tiny-box regression within a unified architecture is propose...
Experiments show that BIDC-YOLO improves Precision, Recall, mAP50, and mAP50-95 by 9.6, 10.4, 13.1, and 8.9 percentage points, respectively, compared with YOLOv8s.
Abstract. In the domain of unmanned aerial vehicle (UAV) aerial imagery, objects frequently exhibit dense and nonuniform distribution patterns, often resulting in false positives and missed detections. To overcome these challenges, we propose SIG-YOLOv8s, an advanced object detection architecture built upon the YOLOv8s...
PRI-Net is proposed, an efficient and lightweight multimodal fusion framework for UAV localization that integrates point cloud splatting, residual attention fusion, and an information bottleneck that achieves high localization accuracy with lightweight architectures, while reducing feature dimensionality and improving...
A lightweight, high-precision framework extending the YOLOv11 architecture, integrating Progressive Channel-wise Self-Attention and Dynamic Tanh, which provides a practical and efficient solution for real-time aerial surveillance at night.
To address the prevalent issue of illegal flights of small unmanned aerial vehicles (UAVs) in low-altitude security scenarios, as well as the critical limitations of general-purpose object detection models—namely insufficient feature extraction capability for small targets and high false positive rates—this paper propo...
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