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基于卷积神经网络的低参数量实时图像分割算法

Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network

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摘要

提出了一种低参数量实时图像语义分割网络模型Atrous-squeezeseg。模型在最低参数量为2.1×107时的运算帧率为45.3 frame/s,像素点准确度与均交并比分别可达到59.5%与62.9%。同时,嵌入式设备NVIDIA TX2的运算帧率可达8.3 frame/s。实验结果表明,相比于其他分割算法,所提模型的速度和参数量均得到了提升。

Abstract

We propose a real-time image semantic segmentation network model, which is named as Atrous-squeezeseg. Under the condition that the minimum parameter of the model is 2.1×107, the operation frame rate is 45.3 frame/s, and the pixel point accuracy and mean intersection over union can reach 59.5% and 62.9%, respectively. At the same time, in the embedded device NVIDIA TX2, the operate frame rate is up to 8.3 frame/s. The experimental results show that, compared with other segmentation algorithms, the speed and parameter quantity of the proposed model are increased.

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中图分类号:TP391

DOI:10.3788/lop56.091003

所属栏目:图像处理

基金项目:浙江大学CAD&CG国家重点实验室开放课题(A1923)、成都市科技项目(2015-HM01-00050-SF)

收稿日期:2018-10-22

修改稿日期:2018-11-29

网络出版日期:2018-11-30

作者单位    点击查看

谭光鸿:西南交通大学信息科学与技术学院, 四川 成都 611756
侯进:西南交通大学信息科学与技术学院, 四川 成都 611756
韩雁鹏:西南交通大学信息科学与技术学院, 四川 成都 611756
罗朔:西南交通大学信息科学与技术学院, 四川 成都 611756

联系人作者:侯进(jhou@swjtu.edu.cn)

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引用该论文

Tan Guanghong,Hou Jin,Han Yanpeng,Luo Shuo. Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091003

谭光鸿,侯进,韩雁鹏,罗朔. 基于卷积神经网络的低参数量实时图像分割算法[J]. 激光与光电子学进展, 2019, 56(9): 091003

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