SenseLite:一个基于YOLO的轻量级模型,用于在空中图像中检测小物体
Tianxin Han1, Qing Dong1, Lina Sun1
1Department of Process Equipment and Control Engineering, School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
SenseLite是一种新的轻量级模型,可以提高空中图像中的小物体检测. 它实现了比YOLOv5更高的准确性和效率,使实时应用成为可能.
科学领域:
- 空中遥感器是空中遥感器.
- 计算机视觉 计算机视觉 计算机视觉
- 深度学习用于对象检测.
背景情况:
- 在航空图像中检测小物体是很困难的,因为细微的特征,复杂的环境和低分辨率.
- 现有的深度学习模型通常需要大量的计算资源,限制了城市规划和交通监控等领域的实时应用.
研究的目的:
- 开发一种轻量级和高效的模型,用于准确和实时检测空中图像中的小物体.
- 改进现有的物体检测框架,如用于空中遥感应用的YOLOv5.
主要方法:
- 推出了SenseLite,一个精简的YOLOv5架构,将Involution纳入骨干,以增强语义和GSConv/slim-Neck在部,以减少复杂性.
- 集成了一个挤压和激发 (SE) 机制,以提高通道通信并提高检测准确度.
- 利用软NMS有效处理重叠检测,以实现精确的并发识别.
主要成果:
- SenseLite将参数降低了30.5% (7.05M至4.9M) 和计算负载 (GFLOPs从15.9至11.2).
- 与YOLOv5.5相比,在DOTA数据集上实现了5.5%的mAP0.5改进,0.9%的更高精度和1.4%的更好的回忆.
- 与其他领先的对象检测方法相比,在空中成像中表现出优越的性能.
结论:
- SenseLite在轻量级和高效的小型物体检测中为空中遥感提供了显著的进步.
- 该模型的精度提高和计算需求降低使实用实时应用成为可能.
- 对于需要精确空中物体识别的关键任务,SenseLite提供了具有竞争力的解决方案.
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