基于跨度融合注意力机制网络,用于街头场景的语义细分
Xin Ye1, Lang Gao1, Jichen Chen2
1Institute of Artificial Intelligence and Data Science, Xi'an Technological University, Xi'an, China.
Frontiers in neurorobotics
|September 18, 2023
概括
在嵌入式系统的语义细分中,CFANet实现了速度和准确性之间的平衡. 这种新的网络融合了跨规模的功能,提高了移动设备的性能.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 语义分割为每个像素分配一个类,这对于图像理解至关重要.
- 由于计算限制,在嵌入式系统和移动设备上部署高精度细分仍然具有挑战性.
- 提高语义细分中的速度-精度权衡是持续的研究需求.
研究的目的:
- 为资源有限的设备开发一个高效的语义细分网络.
- 为了提高语义细分算法的准确性和速度.
- 引入一种新的网络架构,有效地融合多层次特征.
主要方法:
- 拟议的CFANet (跨度融合注意网络),集成来自不同尺度的特征.
- 引入了一个高效的残余模块 (ERM),利用扩展和因数分解的卷积.
- 开发了一个多分支道注意力机制 (MCAM) 用于特征地图的改进.
主要成果:
- 在Cityscapes数据集上,CFANet实现了70.6%的mIoU.
- 在CamVid数据集上,CFANet实现了67.7%的mIoU.
- 在只有0.84M参数的NVIDIA RTX 2080Ti GPU上展示了118 FPS (城市景观) 和105 FPS (CamVid) 的高推断速度.
结论:
- 在嵌入式系统上,CFANet为实时语义细分提供了一个有希望的解决方案.
- 拟议的架构有效地平衡了准确性和计算效率.
- 新型模块 (ERM和MCAM) 对网络的性能做出了重大贡献.
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