首页> 外文期刊>Electronics Letters >Multi-modal neural networks with multi-scale RGB-T fusion for semantic segmentation
【24h】

Multi-modal neural networks with multi-scale RGB-T fusion for semantic segmentation

机译:具有多尺度RGB-T融合的多模态神经网络,用于语义分割

获取原文
获取原文并翻译 | 示例
           

摘要

A novel deep-learning-based method for semantic segmentation of RGB and thermal images is introduced. The proposed method employs a novel neural network design for multi-modal fusion based on multi-resolution patch processing. A novel decoder module is introduced to fuse the RGB and thermal features extracted by separate encoder streams. Experimental results on synthetic and real-world data demonstrate the efficiency of the proposed method compared with state-of-the-art methods.
机译:介绍了一种新的基于深学习的RGB和热图像的语义分割方法。该方法采用了一种基于多分辨率补丁处理的多模态融合的新型神经网络设计。引入了一种新颖的解码器模块,以融合由单独的编码器流提取的RGB和热特征。合成和实世界数据的实验结果表明了与最先进的方法相比拟议方法的效率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号