YOLO-B:基于双融合和高效脱的红外目标检测算法
Yanli Hou1, Bohua Tang1, Zhen Ma1
1School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, PR China.
PloS one
|March 28, 2024
概括
新的YOLO-B红外目标检测算法增强了特征提取和融合,提高了准确性和回忆. 这种先进的模型与现有的YOLO版本相比,为红外目标识别提供了更高的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 现有的YOLOv5s算法因功能提取不完整和检测错误而难以检测红外目标.
- 红外目标检测需要强大的特征表示,以克服像低分辨率和热噪声这样的挑战.
研究的目的:
- 开发一个改进的红外目标检测算法,YOLO-B,解决YOLOv5s.的局限性.
- 为了增强特征提取,信息融合和红外目标的预测准确性.
主要方法:
- 拟议的CSPPF结构扩大特征提取网络的接收场.
- 实现了Bifusion Neck,用于有效地融合浅层和深层特征.
- 利用一个高效的脱头进行预测和WIoUv3损失用于界限框回归.
主要成果:
- 每个提议的改进都单独证明了卓越的检测准确性.
- 与YOLOv5s相比,YOLO-B的精度提高了1.9%,回忆率提高了7.3%,mAP@0.5提高了3.8%,mAP@0.5:0.95提高了4.6%.
- 在参数效率和检测准确度方面,YOLO-B的表现优于YOLOv7和YOLOv8.
结论:
- YOLO-B算法显著提高了红外目标检测性能.
- 功能提取,融合和预测的综合增强有助于算法的有效性.
- 对于红外目标检测应用来说,YOLO-B是一个有前途的进步.
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