基于语义分割网络的路面积水与湿滑区域检测

doi:10.19562/j.chinasae.qcgc.2021.04.005

Abstract

Abstract:

The adhesion coefficient of water?covered or wet road surfaces is much smaller than that of dry road surfaces， which has a great effect on the traffic safety and maneuverability of vehicle， so by timely obtaining the information of road conditions and issuing warning can greatly reduce potential impairments. In this paper， the application of image?based semantic segmentation network to the recognition of water?covered and wet road conditions is studied， which can not only predict the road conditions in the future， but also obtain the distribution of water?covered and wet areas on road. The method proposed utilizes the semantic segmentation network Res-UNet++ to segment the water?covered and wet areas on road pavement. The Res-UNet++ structure includes an embedded encoder?decoder structure of different depths， with a residual structure added on the feature extraction part of the network to make image features easier to learn. The method adopted achieves an average segmentation accuracy of 90.07% in MIoU， and overcomes the defects of other methods.

Key words: detection of water?covered and wet areas, encoder?decoder, deep learning, semantic segmentation network

Hai Wang,Baixiang Cai,Yingfeng Cai,Ze Liu,Kai Sun,Long Chen. Detection of Water⁃covered and Wet Areas on Road Pavement Based on Semantic Segmentation Network[J].Automotive Engineering, 2021, 43(4): 485-491.

Figures/Tables 11

References 21

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方法	IoU/%
背景	97.22
路面	91.04
积水区域	88.35
湿滑区域	83.67
平均	90.07

方法	IoU/%
分类神经网络	90.7
支持向量机(SVM)	87
K?means算法	85
本文方法	90.07

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Detection of Water⁃covered and Wet Areas on Road Pavement Based on Semantic Segmentation Network

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