一个混合注意力多尺度融合网络,用于实时语义细分
Baofeng Ye1,2, Renzheng Xue3,4, Qianlong Wu1,2
1School of Computer and Control Engineering, Qiqihar University, Qiqihar, 161003, China.
Scientific reports
|January 5, 2025
概括
本研究引入了一种新的语义细分方法,可以使用注意力模块 (HFRM和HFFM) 和边缘检测恢复丢失的空间信息. 这种方法提高了准确性,并保持了用于实际应用的高推理速度.
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.
- 图像细分 图像细分
背景情况:
- 语义细分算法经常优先考虑语义信息而不是空间细节,导致准确性损失.
- 当前方法的实时推断速度改进以减少空间信息检索的代价.
研究的目的:
- 提出一种新的语义细分方法,有效地检索丢失的空间信息.
- 为了提高对象分类的准确性和边界信息提取.
- 为了提高推断速度和语义细分中的准确性之间的平衡.
主要方法:
- 设计了一个层次特征融合模块 (HFFM) 来合并多层次特征并使用注意力捕捉更大的受体场.
- 开发了一种层次特征检索模块 (HFRM),结合通道和空间注意力,以恢复下调样本诱导的空间信息丢失.
- 集成边缘检测技术来完善边界信息提取.
主要成果:
- 在城市景观 (512x1024输入) 上以176 FPS实现了73.6%的mIoU.
- 在Camvid (512x1024输入) 上以146 FPS实现了70.0%的mIoU.
- 与现有网络相比,在速度和准确性方面表现出卓越的性能.
结论:
- 提出的方法有效地恢复空间信息,提高语义细分的准确性.
- 集成HFRM和HFFM模块为高精度的实时语义细分提供了一个实用的解决方案.
- 该方法增强了语义细分模型的实用性,用于现实世界的应用.
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