Semantic Segmentation of Autonomous Driving Images by the Combination of Deep Learning and Classical Segmentation

被引:9
作者
Hamian, Mohammad Hosein [1 ]
Beikmohammadi, Ali [2 ]
Ahmadi, Ali [1 ]
Nasersharif, Babak [1 ]
机构
[1] KN Toosi Univ Technol, Fac Comp Engn, Tehran, Iran
[2] Amirkabir Univ Technol, Tehran Polytech, Dept Elect Engn, Tehran, Iran
来源
2021 26TH INTERNATIONAL COMPUTER CONFERENCE, COMPUTER SOCIETY OF IRAN (CSICC) | 2021年
关键词
Image Segmentation; Semantic Segmentation; Autonomous Driving; Deep Neural Network; Conditional Random Field;
D O I
10.1109/CSICC52343.2021.9420573
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
One of the bold issues in autonomous driving is considered semantic image segmentation, which must be done with high accuracy and speed. Semantic segmentation is used to understand an image at the pixel level. In this regard, various architectures based on deep neural networks have been proposed for semantic segmentation of autonomous driving image datasets. In this paper, we proposed a novel combination method in which dividing the image into its constituent regions with the help of classical segmentation brings about achieving beneficial information that improves the DeepLab v3+ network results. The proposed method with the two backbones, Xception and MobileNetV2, obtains the mIoU of 81.73% and 76.31% on the Cityscapes dataset, respectively, which shows promising results compared to the model without post-processing.
引用
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页数:6
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