JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (4): 20-26.doi: 10.6040/j.issn.1672-3961.0.2017.592

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A new method for muti-objects image segmentation based on faster region proposal networks

HUANG Jinchao   

  1. College of Information and Engineering, Longyan University, Longyan 364000, Fujian, China
  • Received:2017-11-23 Online:2018-08-20 Published:2017-11-23

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Abstract: Aim at the shortage of a large amount of redundancy and overlaps of conventional semantic segmentation algorithm, these shortages caused the image segmentation results getting lower accuracy and robust. A new algorithm of multi-objects image segmentation based on faster region proposal networks was proposed. A selective search algorithm was used to get the initial proposal boxes; a faster region proposal network was used to get initial image segmentation boxes. In order to validate our proposed algorithm, the VGG16 models that pre-trained on ImageNet was used on this problem. By using COCO dataset and Cityscapes dataset, the model was well fine-tuned. The test dataset was used for testing semantic segmentation and image segmentation. Compared with YOLO algorithm, the experimental results showed that our proposed algorithm increased mAP of 2.16% and 1.55%. The initial image segmentation boxes by faster region proposal networks were best fitted by GrabCut, multi-objects segmentation results were more accurate and robust. Our proposed algorithm got higher accuracy by sacrifice little time consumption, which got more application scenes in multi-object patterns recognition.

Key words: GrabCut algorithm, faster region proposal network(Faster RPN), selective search algorithm, muti-objects segmentation, image segmentation

CLC Number: 

  • TP391
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