融合自我注意力和CoordConv来改进YOLOv5s算法用于红外弱目标检测
Xiangsuo Fan1,2, Wentao Ding1, Wenlin Qin1
1School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究使用改进的YOLOv5s模型增强了红外弱目标检测,实现了96.7%的mAP. 这种新方法提高了实时应用复杂场景的精度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 卷积神经网络 (CNN) 在目标检测方面表现出色,但与小目标和复杂的背景作斗争.
- 在具有挑战性的环境中红外弱目标检测仍然是现有算法的一个重大障碍.
- 实时检测要求为提高这些场景的准确性增加了复杂性.
研究的目的:
- 在复杂场景中提高红外弱目标检测的准确性.
- 解决标准CNN在识别具有低对比度的小目标方面的局限性.
- 开发一个实时检测解决方案,保持高性能.
主要方法:
- 通过整合瓶变压器结构和CoordConv.修改了YOLOv5s算法.
- 引入了一个新的损失函数,利用2D高斯分布和规范高斯瓦瑟斯坦距离 (NWD).
- 对相关数据集的主流检测算法对改进的模型进行了评估.
主要成果:
- 增强的YOLOv5s模型实现了96.7%的平均平均精度 (mAP).
- 与原来的YOLOv5s.相比,mAP的改善率提高了2.2个百分点.
- 在复杂的背景中检测弱点目标方面表现出卓越的表现.
结论:
- 提议的改进显著提高了红外弱目标检测准确度.
- 结合瓶变压器,CoordConv和NWD损失功能的集成有效地解决了检测挑战.
- 精细的算法为实时,高精度的弱目标检测提供了一个有前途的解决方案.
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