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面向嵌入式平台的轻量级目标检测网络

Light-Weight Object Detection Networks for Embedded Platform

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摘要

基于深度可分离卷积, 提出了一种适用于嵌入式平台的小型目标检测网络MTYOLO(MobileNet Tiny-Yolo), 它将待检测的图片平均分割成多个单元格, 并采用深度可分离卷积代替传统卷积, 减少了参数量和计算量。采用点卷积和特征图融合的方法来提高检测精度。实验结果表明, 所提MTYOLO网络模型大小为41 MB, 约为Tiny-Yolo模型的67%, 其在PASCAL VOC 2007数据集上的检测准确率可达到57.25%, 检测效果优于Tiny-Yolo模型, 更适合应用于嵌入式系统。

Abstract

Based on depth separable convolution, a small object detection network for embedded platform, MTYOLO (MobileNet Tiny-Yolo), is proposed. It divides the image into many grids and replaces the traditional convolution by the depth separable convolution, which decreases the number of parameters and computational cost. The point convolution and the feature map merging are adopted to improve the detection accuracy. The experimental results show that the size of the proposed MTYOLO network model is 41 MB, approximately 67% of that of Tiny-Yolo model. Furthermore, its detection accuracy on the PASCAL VOC 2007 dataset is up to 57.25%, superior to the Tiny-Yolo model’s. The proposed model is particularly suitable for application in embedded platforms.

Newport宣传-MKS新实验室计划
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中图分类号:TP391.4

DOI:10.3788/aos201939.0415006

所属栏目:机器视觉

基金项目:国家自然科学基金(61471110)、中央高校基本科研业务专项资金(N172608005)、辽宁省自然科学基金(20180520040)、沈阳市高层次创新人才支持计划(RC170490)

收稿日期:2018-10-22

修改稿日期:2018-12-06

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作者单位    点击查看

崔家华:东北大学信息科学与工程学院, 辽宁 沈阳 110819
张云洲:东北大学信息科学与工程学院, 辽宁 沈阳 110819东北大学机器人科学与工程学院, 辽宁 沈阳 110819
王争:东北大学信息科学与工程学院, 辽宁 沈阳 110819
刘及惟:东北大学信息科学与工程学院, 辽宁 沈阳 110819

联系人作者:张云洲(zhangyunzhou@mail.neu.edu.cn)

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引用该论文

Cui Jiahua,Zhang Yunzhou,Wang Zheng,Liu Jiwei. Light-Weight Object Detection Networks for Embedded Platform[J]. Acta Optica Sinica, 2019, 39(4): 0415006

崔家华,张云洲,王争,刘及惟. 面向嵌入式平台的轻量级目标检测网络[J]. 光学学报, 2019, 39(4): 0415006

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