基于无人机图像的YOLOv8-MPEB小目标检测算法
Wenyuan Xu1, Chuang Cui1, Yongcheng Ji1
1School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China.
Heliyon
|April 29, 2024
概括
本研究介绍了YOLOv8-MPEB,这是一个在无人机图像中检测小目标的增强模型. 它提高了准确性,减少了模型大小,在关键的空中监视任务中超越了现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 无人驾驶飞行器 (UAV) 的空中图像对目标检测提出了重大挑战,包括尺度变化,小物体大小和复杂的场景.
- 由于这些固有的困难,现有的检测算法在无人机应用程序中经常遭受错过或错误检测.
研究的目的:
- 开发一种高效准确的算法,用于在无人机空中图像中检测小目标.
- 增强YOLOv8模型,以应对无人机图像的特定挑战,提高检测性能和模型效率.
主要方法:
- 拟议的YOLOv8-MPEB算法用轻量级的MobileNetV3.3替换了CSPDarknet53的骨干.
- 在C2f模块内集成了专门的小目标检测层和高效多尺度注意力 (EMA) 机制.
- 在部部分使用双向特征金字塔网络 (BiFPN) 来改进尺度变化处理.
主要成果:
- 在定制头盔和反射服装数据集上,YOLOv8-MPEB算法实现了91.9%的平均平均精度 (mAP).
- 该模型具有739万个参数和14.5MB的大小,与标准YOLOv8.8相比,参数减少了34%,大小减少了32%.
- 与其他普遍的检测算法相比,证明了更高的准确性和速度.
结论:
- 拟议的YOLOv8-MPEB算法有效地提高了无人机空中图像中的小目标检测.
- 该模型在准确性和效率上有了显著的改进,使其适合于现实世界的无人机应用.
- 该研究通过废弃实验和比较分析验证了算法的有效性.
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