DMDNet:用于VDT突出物体检测的双分支多模式深度融合网络.
Yaoqi Sun1, Bin Wan2, Haibing Yin3
1School of Artificial Intelligence, Lishui University, Lishui, 323000, China; Lishui Institute of Hangzhou Dianzi University, Hangzhou Dianzi University, Hangzhou, 310018, China.
概括
本研究介绍了一种双分支深度融合网络 (DMDNet),用于多式联络突出物体检测. 通过在过程中稍后融合特征,DMDNet提高了准确性,降低了噪音并提高了检测性能.
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:
- 在早期编码阶段直接融合多模式特征 (可见,深度,热) 会引入噪声,降低突出物体检测的准确性.
- 现有的方法往往难以有效地整合来自不同传感器模式的互补信息.
研究的目的:
- 为改进突出物体检测提出一个新的双分支多式联络深度融合网络 (DMDNet).
- 通过在解码阶段进行融合来解决早期特征融合中的噪声放大挑战.
主要方法:
- 对于可见图像,DMDNet使用单独的编码器分支,对于深度和热图像,使用联合分支.
- 该网络包括用于深度热互补的模式交互 (MI) 模块,多尺度特征感知 (MFP) 和区域优化 (RO) 模块.
- 一个双分支融合 (DF) 模块集成功能从底到顶,用于最终的突出地图生成.
主要成果:
- 在VDT-2048数据集上,DMDNet表现出卓越的性能.
- 实验结果验证了拟议的网络架构和融合战略的有效性.
结论:
- 拟议的DMDNet通过延迟多模式融合到解码器阶段,有效地减少噪音.
- 网络架构成功地利用可见,深度和热模式的互补特征来增强突出物体检测.
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