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Real-time object segmentation based on convolutional neural network with saliency optimization for picking 被引量:1
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作者 CHEN Jinbo WANG Zhiheng LI Hengyu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第6期1300-1307,共8页
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 regio... 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. 展开更多
关键词 convolutional neural network object detection object segmentation superpixel saliency optimization
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