MSConv-YOLO:基于YOLOv8的改进的小目标检测算法
Linli Yang1,2, Barmak Honarvar Shakibaei Asli2
1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Journal of imaging
|August 27, 2025
概括
这项研究使用MultiScaleConv-YOLO (MSConv-YOLO) 增强了YOLOv8s在无人机图像中检测小物体的功能. 改进的模型提高了复杂的空中场景中小型目标的检测准确性和回忆.
科学领域:
- 计算机视觉
- 人工智能
- 遥感技术
背景情况:
- 在无人机 (UAV) 空中影像中检测小物体是具有挑战性的,因为尺寸差异和复杂的背景.
- 像YOLOv8s这样的现有框架需要改进以实现小目标的最佳性能.
研究的目的:
- 提高无人机空中影像中小物体检测的性能.
- 为YOLOv8s框架引入实际的工程增强.
主要方法:
- 通过集成一个MultiScaleConv (MSConv) 模块来增强多尺度特征提取,开发了MultiScaleConv-YOLO (MSConv-YOLO).
- 用WIoU v3取代了CIoU损失以改善小目标的边界框回归.
- 嵌入了高分辨率检测头在部和头部结构,以保持细粒度的特征.
主要成果:
- 与VisDrone2019数据集的基线YOLOv8s相比,MSConv-YOLO实现了mAP@0.5的6.9%改善和回忆的6.3%增长.
- 废除研究证实了个别增强剂的有效性.
结论:
- 在无人机场景中,MSConv-YOLO提供了一种实用且有效的小物体检测解决方案.
- 提议的改进提高了检测性能,但没有从根本上改变YOLOv8s架构.
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