Efficient Instance and Semantic Segmentation for Automated Driving

被引:0
|
作者
Petrovai, Andra [1 ]
Nedevschi, Sergiu [1 ]
机构
[1] Tech Univ Cluj Napoca, Dept Comp Sci, Cluj Napoca, Romania
来源
2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19) | 2019年
基金
欧盟地平线“2020”;
关键词
D O I
10.1109/ivs.2019.8814177
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Environment perception for automated vehicles is achieved by fusing the outputs of different sensors such as cameras, LIDARs and RADARs. Images provide a semantic understanding of the environment at object level using instance segmentation, but also at background level using semantic segmentation. We propose a fully convolutional residual network based on Mask R-CNN to achieve both semantic and instance level recognition. We aim at developing an efficient network that could run in real-time for automated driving applications without compromising accuracy. Moreover, we compare and experiment with two different backbone architectures, a classification type of network and a faster segmentation type of network based on dilated convolutions. Experiments demonstrate top results on the publicly available Cityscapes dataset.
引用
收藏
页码:2575 / 2581
页数:7
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