一种多层特征融合的道路场景实时分割方法

doi:10.13474/j.cnki.11-2246.2020.0003

测绘通报 ›› 2020, Vol. 0 ›› Issue (1): 10-15.doi: 10.13474/j.cnki.11-2246.2020.0003

一种多层特征融合的道路场景实时分割方法

周继苗¹, 李必军^1,2, 陈世增¹

1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
2. 时空数据智能获取技术与应用教育部工程研究中心, 湖北武汉 430079

收稿日期:2019-09-04 发布日期:2020-02-10
通讯作者: 李必军。E-mail:lee@whu.edu.cn E-mail:lee@whu.edu.cn
作者简介:周继苗(1994-),男,硕士生,主要从事无人驾驶和计算机视觉方面的研究。E-mail:jimiaozhou@whu.edu.cn
基金资助:
国家自然科学基金（41671441）；国家自然科学基金汽车产业创新发展联合基金（U1764262）

A real time semantic segmentation method based on multi-level feature fusion

ZHOU Jimiao¹, LI Bijun^1,2, CHEN Shizeng¹

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. Engineering Research Center for Spatial-temporal Data Smart Acquisition and Application, Ministry of Education of China, Wuhan University, Wuhan 430079, China

Received:2019-09-04 Published:2020-02-10

摘要/Abstract

摘要： 道路场景理解是自动驾驶领域中重要模块之一，它可以提供关于道路更丰富的信息，对于建立高精度地图和实时规划都具有重要作用。其中，语义分割可以为图像每个像素赋予类别信息，是自动驾驶场景理解中最常用的方法。但是，目前常用的语义分割算法在速度和精度上大都不能达到很好的平衡。本文在MobileNetV2的基础上，提出了一种多层次特征融合的方法，使得网络可以在实时运行的同时保证精度满足实际应用的需求，并在Cityscapes数据集上进行了试验验证和分析。

关键词: 自动驾驶, 语义分割, 场景理解, 深度学习, 多尺度特征融合

Abstract: Road scene understanding is an important module in autonomous driving as it can provide rich information for other modules like high-res map and dynamic planning. Semantic segmentation classifies each pixel in an image and it is often used in scene understanding. However, common segmentation algorithms cannot balance the accuracy and speed. In this paper, we first extract features with light-weight backbone MobileNetV2, and then introduce a new feature fusion module to combine multi-scale features efficiently, which can makes a good balance between accuracy and speed. Finally, we evaluate our method on the Cityscapes dataset.

Key words: autonomous driving, semantic segmentation, scene understanding, deep learning, multi-scale feature fusion

中图分类号:

P228

周继苗, 李必军, 陈世增. 一种多层特征融合的道路场景实时分割方法[J]. 测绘通报, 2020, 0(1): 10-15.

ZHOU Jimiao, LI Bijun, CHEN Shizeng. A real time semantic segmentation method based on multi-level feature fusion[J]. Bulletin of Surveying and Mapping, 2020, 0(1): 10-15.

参考文献

[1] 李琳辉, 钱波, 连静, 等. 基于卷积神经网络的交通场景语义分割方法研究[J]. 通信学报, 2018, 39(4):123-130.
[2] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Image net classification with deep convolutional neural networks[C]//Advances in neural information processing systems. Lake Tahoe:MITPress, 2012:1097-1105.
[3] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[4] LONG J, SHELHAMER E, DARRELL T. Fully convolu-tional networks for semantic segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Boston:IEEE, 2015:3431-3440.
[5] CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European conference on computer vision (ECCV). Munich:Springer, 2018:801-818.
[6] YU F, KOLTUN V. Multi-Scale context aggregation by dilated convolutions[J/OL]. 2016-04-30[2019-08-21].https://arxiv.org/abs/1511.07122.
[7] ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. HonoluluI:IEEE, 2017:2881-2890.
[8] PASZKE A, CHAURASIA A, KIM S, et al. Enet:a deep neural network architecture for real-time semantic segmentation[J/OL]. 2016-06-07[2019-08-21].https://arxiv.org/abs/1606.02147.
[9] MEHTA S, RASTEGARI M, CASPI A, et al. Espnet:efficient spatial pyramid of dilated convolutions for semantic segmentation[C]//Proceedings of the European Conference on Computer Vision (ECCV). Munich:Springer, 2018:552-568.
[10] SANDLER M, HOWARD A, ZHU M, et al. Mobilenetv2:inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City:IEEE, 2018:4510-4520.
[11] CORDTS M, OMRAN M, RAMOS S, et al. The cityscapes dataset for semantic urban scene understanding[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas:IEEE, 2016:3213-3223.
[12] CHOLLET F. Xception:deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu:IEEE, 2017:1251-1258.
[13] LIN M, CHEN Q, YAN S. Network in network[J/OL].2014-03-04[2019-08-22].https://arxiv.org/abs/1312.4400.
[14] ZEILER M D, FERGUS R. Visualizing and understanding convolutional network[C]//European conference on computer vision. Zurich:Springer, 2014:818-833.
[15] PENG C, ZHANG X, YU G, et al. Large kernel matters-improve semantic segmentation by global convolutional network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Honolulu:IEEE, 2017:4353-4361.
[16] YU C, WANG J, PENG C, et al. Bisenet:bilateral segmentation network for real-time semantic segmentation[C]//Proceedings of the European Conference on Computer Vision (ECCV). Munich:Springer, 2018:325-341.
[17] BADRINARAYANAN V, KENDALL A, CIPOLLA R. Segnet:a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(12):2481-2495.

一种多层特征融合的道路场景实时分割方法

A real time semantic segmentation method based on multi-level feature fusion

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