(TensorFlow)实时语义分割比较研究

                                                                                                                                                                                 

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摘要

 

转自：爱可可-爱生活

论文《RTSeg: Real-time Semantic Segmentation Comparative Study》摘要：

Semantic segmentation benefits robotics related applications especially autonomous driving. Most of the research on semantic segmentation is only on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. The few work conducted in this direction does not provide principled methods to evaluate the different design choices for segmentation. In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods. The framework is comprised of different network architectures for feature extraction such as VGG16, Resnet18, MobileNet, and ShuffleNet. It is also comprised of multiple meta-architectures for segmentation that define the decoding methodology. These include SkipNet, UNet, and Dilation Frontend. Experimental results are presented on the Cityscapes dataset for urban scenes. The modular design allows novel architectures to emerge, that lead to 143x GFLOPs reduction in comparison to SegNet. This benchmarking framework will be made publicly available at ¹ .

The repository contains the official TensorFlow code used in the our paper RTSEG: REAL-TIME SEMANTIC SEGMENTATION COMPARATIVE STUDY for comparing different realtime semantic segmentation architectures.

Description

Semantic segmentation benefits robotics related applications especially autonomous driving. Most of the research on semantic segmentation is only on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. The few work conducted in this direction does not provide principled methods to evaluate the different design choices for segmentation. In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods. The code and the experimental results are presented on the CityScapes dataset for urban scenes.

论文链接：

https://www.arxiv-vanity.com/papers/1803.02758/

代码链接：

https://github.com/MSiam/TFSegmentation

原文链接：

https://m.weibo.cn/1402400261/4216793734309974

“完整内容”请点击【阅读原文】

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