TL;DR
This paper presents an advanced approach to object detection and tracking within the realm of computer vision for UAVs that integrates YOLOv8x, Nano Instance Segmentation, and Dueling Double Deep Q Network.
Multiple object tracking (MOT) from unmanned aerial vehicles (UAVs) presents significant challenges due to drastic viewpoint changes and ambiguous top-down appearances. The former often result in large, misleading displacements of objects in the image plane, while the latter makes it difficult to distinguish between ob...
Detecting tiny objects in UAV imagery poses persistent challenges due to severe information loss during backbone downsampling, irregular target geometry, and the failure of global inference to resolve densely clustered micro-objects. We propose a three-component detection framework where each module addresses a specifi...
The results validate the effectiveness of the method in addressing the specific challenges of UAV-based object detection, offering a balanced solution for accuracy and efficiency in resource-constrained scenarios.
To address the issues of minuscule scale, complex backgrounds, and severe occlusions in small object detection within unmanned aerial vehicle (UAV) images, this paper proposes an improved model based on YOLOv11n, named RMD-YOLO11n. The model introduces the C3k2-RFAConv module into the backbone network. By leveraging th...
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.
Experiments show that MDF-YOLO provides a favorable balance between detection accuracy and model complexity and exhibits potential for practical UAV-based object detection applications, and category-wise evaluation further demonstrates consistent AP@50 improvements across all 10 object categories.
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