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.
SIG-YOLOv8s, an advanced object detection architecture built upon the YOLOv8s framework, is proposed, demonstrating that SIG-YOLOv8s achieves superior performance in object detection and recognition tasks.
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.
An improved small UAV detection algorithm based on YOLOv11n, namely A DenseBlock-Enhanced YOLOv11 (YOLOv11-Dense), which strengthens the fusion of deep semantic features and shallow spatial features, mitigates the problem of small target feature loss in deep networks, and further improves the detection accuracy of smal...
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