TL;DR
A new real-time detector for aerial imagery based on YOLO11, named RSS-YOLO, designed to replace the C3k2 module in YOLO11 and alleviates insufficient integration of spatial and semantic information within the feature extraction layers.
Object detection in unmanned aerial vehicle (UAV)-based visual sensing is important for aerial monitoring and intelligent perception. However, it remains difficult because camera-captured aerial images often contain small targets, cluttered backgrounds, occlusion, and limited edge-computing resources. We propose LFC-YO...
This work proposes LSO-YOLO, a novel real-time detector optimized for UAV-based small-object detection that significantly reduces parameter count and computational cost by approximately 67% and 18.18%, respectively, while maintaining a high inference speed of 126 FPS on an NVIDIA RTX 3060 GPU, meeting the requirements...
SPAF, an enhanced YOLO-based model built upon YOLO26 that improves object detection through the integration of a multi-scale attention mechanism, progressive feature aggregation, and adaptive feature fusion, is proposed.
The proposed AeroFineFusion-Det introduces a fine-scale bidirectional multi-scale fusion strategy to improve information flow across feature levels, together with a dual-attention weighted fusion mechanism that adaptively enhances discriminative representations.
Unmanned aerial vehicles (UAVs) have been widely used in defense, precision agriculture, ecological monitoring, and intelligent transportation because of their compact size, high mobility, and flexible deployment. Object detection based on UAV imagery is a key technique for autonomous perception and mission execution....
SSM-YOLO11s is proposed, a lightweight model optimized for small object detection in aerial imagery that achieves a superior balance between precision and efficiency compared to state-of-the-art models.
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