基于区块自适应特征融合的图像实时语义分割

黄庭鸿; 聂卓赟; 王庆国; 李帅; 晏来成; 郭东生

doi:10.16383/j.aas.c180645

基于区块自适应特征融合的图像实时语义分割

doi: 10.16383/j.aas.c180645

黄庭鸿^1,,
聂卓赟^1, ,,
王庆国^2,,
李帅^3,,
晏来成^1,,
郭东生^1,

1.
华侨大学信息科学与工程学院厦门 361021 中国
2.
约翰内斯堡大学智能系统研究所约翰内斯堡 2146 南非
3.
香港理工大学香港 999077 中国

基金项目:

国家自然科学基金 61403149

华侨大学中青年教师科研提升资助计划项目 ZQN-PY408

华侨大学中青年教师科研提升资助计划项目 Z14Y0002

华侨大学研究生科研创新基金 17013082039

详细信息

作者简介:
黄庭鸿  华侨大学信息科学与工程学院硕士研究生. 2017年获得华侨大学学士学位. 主要研究方向为强化学习和深度学习.E-mail: 063mi@163.com

王庆国  南非约翰内斯堡大学智能系统研究所教授, 新加坡国立大学教授. 1987年获得浙江大学博士学位. 主要研究方向为复杂系统的建模, 估计预测、控制和优化.E-mail: wangqg02286@gmail.com

李帅  香港理工大学研究助理副教授. 2014年获得史蒂文斯理工学院博士学位. 主要研究方向为动态神经网络, 无线传感器网络, 机器人网络, 机器学习和在图上定义的其他动态问题. E-mail: shuaili@polyu.edu.hk

晏来成  华侨大学信息科学与工程学院讲师. 2007年获得重庆大学硕士学位. 主要研究方向为机器人控制, 机器视觉和机器学习. E-mail: ylaicheng@126.com

郭东生  华侨大学信息科学与工程学院副教授. 2015年获得中山大学博士学位. 主要研究方向为机器人控制, 神经网络和数值方法.E-mail: gdongsh@hqu.edu.cn

通讯作者:
聂卓赟华侨大学信息科学与工程学院副教授. 2012年获中南大学博士学位. 主要研究方向为鲁棒控制, 系统建模与辨识. 本文通信作者.E-mail: yezhuyun2004@sina.com

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出版历程
- 收稿日期: 2018-10-01
- 录用日期: 2018-12-25
- 刊出日期: 2021-05-21

Real-time Image Semantic Segmentation Based on Block Adaptive Feature Fusion

1.
College of Information Science and Engineering, National Huaqiao University, Xiamen 361021, China
2.
Institute for Intelligent Systems, University of Johannesburg, Johannesburg 2146, South Africa
3.
the Hong Kong Polytechnic University, Hong Kong 999077, China

Funds:

National Natural Science Foundation of China 61403149

Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University ZQN-PY408

Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University Z14Y0002

Postgraduates' Innovative Fund in Scientific Research of Huaqiao University 17013082039

More Information

Author Bio:
HUANG Ting-Hong  Master student at the College of Information Science and Engineering, National Huaqiao University. He received his bachelor degree from National Huaqiao University in 2017. His research interest covers reinforcement learning and deep learning

WANG Qing-Guo  Professor at the Institute for Intelligent Systems, University of Johannesburg, South Africa, and National University of Singapore, Singapore. He received his Ph.D. degree from Zhejiang University in 1987. His research interest covers modeling, estimation, prediction, control and optimization for complex systems

LI Shuai  Research assistant professor at the Hong Kong Polytechnic University. He received his Ph.D. degree from Stevens Institute of Technology in 2014. His research interest covers dynamic neural networks, wireless sensor networks, robotic networks, machine learning, and other dynamic problems defined on a graph

YAN Lai-Cheng  Lecturer at the College of Information Science and Engineering, National Huaqiao University. He received his master degree from Chongqing University in 2007. His research interest covers robot control, machine vision, and machine learning

GUO Dong-Sheng  Associate professor at the College of Information Science and Engineering, National Huaqiao University. He received his Ph.D. degree from Sun Yat-sen University in 2015. His research interest covers robot control, neural networks, and numerical methods

Corresponding author: NIE Zhuo-Yun Associate professor at the College of Information Science and Engineering, National Huaqiao University. He received his Ph.D. degree from Central South University in 2012. His research interest covers robust control and system modeling and identification. Corresponding author of this paper

摘要

摘要: 近年来结合深度学习的图像语义分割方法日益发展, 并在机器人、自动驾驶等领域中得到应用. 本文提出一种基于区块自适应特征融合(Block adaptive feature fusion, BAFF) 的实时语义分割算法, 该算法在轻量卷积网络架构上, 对前后文特征进行分区块自适应加权融合, 有效提高了实时语义分割精度. 首先, 分析卷积网络层间分割特征的感受野对分割结果的影响, 并在跳跃连接结构(SkipNet) 上提出一种特征分区块加权融合机制; 然后, 采用三维卷积进行层间特征整合, 建立基于深度可分离的特征权重计算网络. 最终, 在自适应加权作用下实现区块特征融合. 实验结果表明, 本文算法能够在图像分割的快速性和准确性之间做到很好的平衡, 在复杂场景分割上具有较好的鲁棒性.
- 深度学习 /
- 实时语义分割网络 /
- 区块自适应特征融合 /
- 跳跃连接结构
Abstract: Recently, image semantic segmentation has made great progress with deep learning, which benefits robotics and automatic driving vehicle. This paper proposes a real-time semantic segmentation algorithm based on block adaptive feature fusion (BAFF). Under the framework of a light convolutional network, a block adaptive feature fusion algorithm is proposed in the context-embedding module, to improve the accuracy of real-time semantic segmentation. First, the problem caused by the different size of receptive field in layers is analyzed, and a feature fusion mechanism with block weight is presented on SkipNet. Then, layers' feature integration is carried on by three-dimension convolution. The feature-weights are calculated by an additional network with depthwise-separable-convolutions (DSC). Finally, the features are fused under adaptive weights. Experiments show that this method obtains excellent segmentation results with a good balance between rapidity and accuracy and owns robustness on segmentation of complex scenes.
- Deep learning /
- real-time semantic segmentation network /
- block adaptive feature fusion (BAFF) /
- SkipNet
Recommended by Associate Editor LIU Cheng-Lin
注释:

1) 本文责任编委刘成林

HTML全文

图 1 区块特征融合与SkipNet叠加融合对比图

Fig. 1 The comparison chart of block feature fusion and SkipNet additive fusion

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图 2 编码—解码结构

Fig. 2 The structure chart of encoding-decoding

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图 3 不同卷积层的语义分割测试

Fig. 3 The test of semantic segmentation for different convolution layer

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图 4 基于BAFF的语义分割网络结构

Fig. 4 The structure chart of the semantic segmentation network based on BAFF

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图 5 CNN预测精度与物体大小关系

Fig. 5 The relation between the precision of CNN prediction and the size of objects

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图 6 区块加权融合效果

Fig. 6 The effect of block weighted fusion

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图 7 模型训练损失值的变化

Fig. 7 The loss of value during the model training

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图 8 特征融合的显著图

Fig. 8 The salient region of the feature fusion

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图 9 模型精确度对比图

Fig. 9 The comparison chart of model accuracy

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图 10 语义分割效果图对比

Fig. 10 Semantic segmentation effect contract graph

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表 1 加入BAFF前后的模型复杂度对比

Table 1 Comparisons of model complexity before and after adding BAFF

模型	MIoU (%)	运算量(M)	参数量(K)
SkipNet	66.8	15 962.99	841.76
BAFF-SkipNet	70.5	15 963.23	843.17

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表 2 语义分割各类别精度对比(%)

Table 2 Semantic segmentation accuracy comparison of different types (%)

模型	road	swalk	build.	wall	fence	pole	tlight	sign	veg.	terrain	sky	person	rider	car	truck	bus	train	mbike	bike
本文算法	93.0	79.3	86.6	60.2	65.3	62.0	60.2	64.3	89.9	65.0	92.7	72.1	56.0	89.1	58.5	65.1	55.4	53.8	72.3
ENet^[28]	96.3	74.2	85.0	32.1	33.2	43.4	34.1	44.0	88.6	61.4	90.6	65.5	38.4	90.6	36.9	50.5	48.0	38.8	55.4
ContextNet^[29]	97.6	79.2	88.8	43.8	42.8	37.9	52.0	58.8	90.0	66.9	91.9	72.1	53.9	91.6	54.0	66.4	58.3	48.9	61.0
ERFNet^[30]	97.7	81.0	89.8	42.5	48.0	56.3	59.8	65.3	91.4	68.2	94.2	76.8	57.1	92.8	50.8	60.1	51.8	47.3	61.6

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表 3 实时语义分割模型精度对比

Table 3 Accuracy comparison of real-time semantic segmentation models

模型	MIoU (%)	运行时间(ms)	参数量(M)
本文算法	70.5	19.01	0.82
ENet^[28]	58.3	11.82	0.37
ERFNet^[30]	68.0	19.64	2.18

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