LI-YOLOv8:用于远程传感图像的轻量级小目标检测算法,它结合了GSConv和PConv
Pingping Yan1, Xiangming Qi1, Liang Jiang2
1Liaoning Technical University, School of Software, Huludao, Liaoning, China.
PloS one
|May 23, 2025
概括
这项研究介绍了LI-YOLOv8,这是一种用于检测远程传感图像中小目标的轻量级算法. 它显著提高了检测准确度,同时降低了计算成本和参数,优于现有方法.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 在遥感图像中检测小目标面临着特征提取,复杂背景和高计算需求的挑战.
- 现有的算法在各种遥感场景中经常在小物体的准确性和效率上扎.
研究的目的:
- 开发一种轻量级和高效的算法,用于在遥感图像中检测小目标.
- 增强特征提取并减少计算复杂性,以提高检测性能.
主要方法:
- 拟议的LI-YOLOv8算法,是一种修改后的YOLOv8n,结合了GSConv,PConv,RFAConv和多尺度注意力 (EMA) 机制.
- 用ReLU取代SiLU激活,并集成了一个轻量级的GP-Detect头.
- 引入了Inner-Wise IoU损失功能,将Inner-IoU和Wise-IoU v3.3结合起来.
主要成果:
- 与YOLOv8.8.相比,在RSOD数据集上实现了mAP@0.5 (7.6%) 和mAP@0.5:0.95 (2.1%) 的显著改进.
- 降低了10.0%的参数和23.2%的GFLOPs,证明了效率的提高.
- 在不同的数据集中展示了强大的概括性能,如TinyPerson,LEVIR-ship,脑瘤和smoke_fire_1.1.
结论:
- LI-YOLOv8在轻量级小型目标检测中提供了实质性的进步,用于遥感.
- 该算法有效地平衡了高检测准确度与减少的计算资源.
- 提出的方法证明了强大的一般化能力,用于各种现实世界的应用.
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