基于脱动态波器和挤压激发模块的深度双分辨率道路场景细分网络
Hongyin Ni1,2, Shan Jiang1
1School of Computer Science, Northeast Electric Power University, Jilin 132012, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
深度双分辨率道路场景细分网络 (DDF&SE-DDRNet) 通过增强道路场景细分来改善自动驾驶. 这种网络实现了更高的准确性,并保持了实时应用程序的令人满意的推断速度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自主驾驶系统 自主驾驶系统
背景情况:
- 图像语义细分对于自动驾驶辅助至关重要.
- 挑战包括复杂的道路场景和实时处理需求.
研究的目的:
- 提出一个新的网络,深度双分辨率道路场景细分网络 (DDF&SE-DDRNet),以应对细分的挑战.
- 为了提高对自动驾驶应用的细分精度和效率.
主要方法:
- DDF&SE-DDRNet包含一个脱的动态过器,以减少参数,并使卷积内核的动态重量调整成为可能.
- 整合Squeeze-and-Excitation模块允许本地特征地图获得全球特征,减轻本地图像干扰.
主要成果:
- 城市景观数据集的实验结果表明,与现有算法相比,细分精度至少提高了2%.
- DDF&SE-DDRNet实现了令人满意的推断速度,适合实时应用.
结论:
- 拟议的DDF&SE-DDRNet有效地提高了道路场景细分的准确性和效率.
- 该网络为自动驾驶辅助技术面临的挑战提供了一个有希望的解决方案.
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