Real-time object segmentation based on convolutional neural network with saliency optimization for picking

被引：0
|
作者：
CHEN Jinbo [1 ]
WANG Zhiheng [1 ]
LI Hengyu [1 ]
机构：
[1] School of Mechatronic Engineering and Automation, Shanghai University
来源：
Journal of Systems Engineering and Electronics | 2018年 / 29卷 / 06期
基金：
中国国家自然科学基金;
关键词：
convolutional neural network; object detection; object segmentation; superpixel; saliency optimization;
D O I：
暂无
中图分类号：
TP183 [人工神经网络与计算]; TP391.41 [];
学科分类号：
080203 ; 081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method.By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.
引用
收藏
页码：1300 / 1307
页数：8
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