Fast Road Segmentation via Uncertainty-aware Symmetric Network

• URL: http://arxiv.org/abs/2203.04537v1
• Date: Wed, 9 Mar 2022 06:11:29 GMT
• Title: Fast Road Segmentation via Uncertainty-aware Symmetric Network
• Authors: Yicong Chang, Feng Xue, Fei Sheng, Wenteng Liang, Anlong Ming
• Abstract summary: Prior methods cannot achieve high inference speed and high accuracy in both ways. The different properties of RGB and depth data are not well-exploited, limiting the reliability of predicted road. We propose an uncertainty-aware symmetric network (USNet) to achieve a trade-off between speed and accuracy by fully fusing RGB and depth data.
• Score: 15.05244258071472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The high performance of RGB-D based road segmentation methods contrasts with their rare application in commercial autonomous driving, which is owing to two reasons: 1) the prior methods cannot achieve high inference speed and high accuracy in both ways; 2) the different properties of RGB and depth data are not well-exploited, limiting the reliability of predicted road. In this paper, based on the evidence theory, an uncertainty-aware symmetric network (USNet) is proposed to achieve a trade-off between speed and accuracy by fully fusing RGB and depth data. Firstly, cross-modal feature fusion operations, which are indispensable in the prior RGB-D based methods, are abandoned. We instead separately adopt two light-weight subnetworks to learn road representations from RGB and depth inputs. The light-weight structure guarantees the real-time inference of our method. Moreover, a multiscale evidence collection (MEC) module is designed to collect evidence in multiple scales for each modality, which provides sufficient evidence for pixel class determination. Finally, in uncertainty-aware fusion (UAF) module, the uncertainty of each modality is perceived to guide the fusion of the two subnetworks. Experimental results demonstrate that our method achieves a state-of-the-art accuracy with real-time inference speed of 43+ FPS. The source code is available at https://github.com/morancyc/USNet.

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