Chapter
Dec 14, 2021
Enhanced ERFNet Decoder for Road Segmentation Model
Authors: Ning Wang [email protected], Ping Sun [email protected], Jie Zhao [email protected], and Jihai Xu [email protected]Author Affiliations
Publication: CICTP 2021
ABSTRACT
This study presents a road segmentation model with high accuracy and real-time performance. Road segmentation is a crucial problem in the application of autonomous vehicles, which requires real-time performance and accuracy. Nowadays, current models require high-resolution images to realize high accuracy performance, while the low-resolution input losses many details. However, high-resolution input creates much inference time and can’t meet the real-time requirement. To handle this problem, based on Efficient Residual Factorized ConvNet (ERFNet), we insert the feature fusion and enhance its upsampling blocks, which capture more of the roads edge information and achieve higher accuracy. This enhanced ERFNet decoder is designed with three main components: multi-scale feature fusion, revised factorized layers and dense upsampling convolutions. The proposed model shows impressing results on both inference time and segmentation accuracy, and this efficient model can be applied to autonomous vehicle systems.
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Published online: Dec 14, 2021
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1School of Software Engineering, Tongji Univ., Shanghai, China. Email: [email protected]
2School of Software Engineering, Tongji Univ., Shanghai, China. Email: [email protected]
3Boden Intelligent Technology Co., Ltd., Ningbo, Zhejiang, China. Email: [email protected]
4Boden Intelligent Technology Co., Ltd., Ningbo, Zhejiang, China. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.