基于自适应注意力和深度融合的语义细分网络,使用多尺度扩展卷积金字塔.
Shan Zhao1, Zihao Wang1, Zhanqiang Huo1
1School of Software, Henan Polytechnic University, Jiaozuo 454000, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了SDAMNet,这是一个用于语义细分的新型深度学习模型. SDAMNet增强了上下文信息和功能融合,大大提高了复杂场景的细分精度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 语义细分的深度学习模型与缺乏上下文,功能表达不佳和低分辨率的高层特征作斗争.
- 这些局限性导致边界划定不准确,区域错误分类,以及复杂场景中小物体或重叠物体的困难.
研究的目的:
- 提出一种基于自适应注意力和深度融合的新型语义细分网络,与多尺度扩展卷积金字塔 (SDAMNet) 进行深度融合.
- 通过增强上下文信息,功能丰富性和分辨率来解决现有的语义细分方法的局限性.
主要方法:
- 开发了扩展卷积心脏空间金字塔聚合 (DCASPP) 模块,以丰富上下文信息.
- 引入了语义通道空间细节模块 (SCSDM),用于多尺度的特征融合和适应性特征选择,以提高感知能力.
- 构建了语义特征融合模块 (SFFM),以减轻低级特征中的语义缺陷和高级特征中的低分辨率.
主要成果:
- SDAMNet在欧盟的平均交叉点 (MIOU) 中显示出显著的改进.
- 与Deeplabv3+网络相比,在两个基准数据集上实现了2.89%和2.13%的性能增长.
结论:
- SDAMNet有效地提高了语境理解和特征表示,用于语义细分.
- 拟议的网络提供了更高的准确性和稳定性,特别是在复杂的环境场景中.
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