A Dense Feature Pyramid Network-Based Deep Learning Model for Road Marking Instance Segmentation Using MLS Point Clouds

Siyun Chen; Zhenxin Zhang; Ruofei ZhongLiqiang ZhangHao MaLirong Liu

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A Dense Feature Pyramid Network-Based Deep Learning Model for Road Marking Instance Segmentation Using MLS Point Clouds

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摘要

Accurate and efficient extraction of road marking plays an important role in road transportation engineering, automotive vision, and automatic driving. In this article, we proposed a dense feature pyramid network (DFPN)-based deep learning model, by considering the particularity and complexity of road marking. The DFPN concatenated its shallow feature channels with deep feature channels so that the shallow feature maps with high resolution and abundant image details can utilize the deep features. Thus, the DFPN can learn hierarchical deep detailed features. The designed deep learning model was trained end to end for road marking instance extraction with mobile laser scanning (MLS) point clouds. Then, we introduced the focal loss function into the optimization of deep learning model in road marking segmentation part, to pay more attention to the hard-classified samples with a large extent of background. In the experiments, our method can achieve better results than state-of-the-art methods on instance segmentation of road markings, which illustrated the advantage of the proposed method.

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《IEEE Transactions on Geoscience and Remote Sensing》 |2021年第1期|784-800|共17页
作者
Siyun Chen; Zhenxin Zhang; Ruofei ZhongLiqiang ZhangHao MaLirong Liu;
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作者单位

Beijing Advanced Innovation Center for Imaging Theory and Technology, Key Laboratory of 3D Information Acquisition and Application, MOE, and College of Resource Environment and Tourism, Capital Normal University, Beijing, China;

Geographical Science, the State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, China;

Geo-Vision Tech. Corporation, Ltd., Beijing, ChinaLand Satellite Remote Sensing Application Center, MNR, Beijing, China;

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正文语种英语
中图分类地球物理学;遥感技术;
关键词
Roads; Feature extraction; Three-dimensional displays; Deep learning; Image segmentation; Data mining; Remote sensing; dense feature pyramid network (DFPN); instance segmentation; mobile laser scanning (MLS) point clouds; road markings;

A Dense Feature Pyramid Network-Based Deep Learning Model for Road Marking Instance Segmentation Using MLS Point Clouds

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