语义分割--Efficient Deep Models for Monocular Road Segmentation
Efficient Deep Models for Monocular Road Segmentation code: https://lmb.informatik.uni-freiburg.de/Publications/2016/OB16b/ 针对路面检测和分割问题，本文结合FCN 和 U-Net 提出一个网络 Up-Convolutional Networks，在速度和精度方面得到不错的效果
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相关文章
- 1. 图像分割“Efficient Deep Models for Monocular Road Segmentation”
- 2. 语义分割--Efficient and Robust Deep Networks for Semantic Segmentation
- 3. 语义分割--LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
- 4. 语义分割--Global Deconvolutional Networks for Semantic Segmentation
- 5. 【语义分割】Dual Attention Network for Scene Segmentation
- 6. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation 视频语义分割
- 7. 语义分割-- SegNet：A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 8. 语义分割--Deep Dual Learning for Semantic Image Segmentation
- 9. 语义分割--Understanding Convolution for Semantic Segmentation
- 10. 语义分割--Fully Convolutional DenseNets for Semantic Segmentation