Research on Fuzzy Image Instance Segmentation Based on Improved Mask R-CNN

Weidong CHEN; Weiran GUO; Hongwei LIU; Qiguang ZHU

doi:10.11999/JEIT190604

Volume 42 Issue 11

Nov. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(11): 2805-2812

Weidong CHEN, Weiran GUO, Hongwei LIU, Qiguang ZHU. Research on Fuzzy Image Instance Segmentation Based on Improved Mask R-CNN[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2805-2812. doi: 10.11999/JEIT190604

Citation:

Weidong CHEN, Weiran GUO, Hongwei LIU, Qiguang ZHU. Research on Fuzzy Image Instance Segmentation Based on Improved Mask R-CNN[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2805-2812. doi: 10.11999/JEIT190604

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Research on Fuzzy Image Instance Segmentation Based on Improved Mask R-CNN

doi: 10.11999/JEIT190604

Weidong CHEN^{1, 2},
Weiran GUO¹,
Hongwei LIU¹,
Qiguang ZHU^{1, 2
,
,}

1.
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2.
Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao 066004, China

Funds: The National Natural Science Foundation of China (61773333), The Key Project of Science and Technology Plan of Colleges and Universities of Hebei Provincial Department of Education (ZD2018234)

Received Date: 2019-08-08
Rev Recd Date: 2020-08-26

Available Online: 2020-09-03

Publish Date: 2020-11-16

Abstract

Abstract

Mask R-CNN is a relatively mature method for image instance segmentation at this stage. For the problems of segmentation boundary accuracy and poor robustness of fuzzy pictures in Mask R-CNN algorithm, an improved Mask R-CNN method for image instance segmentation is proposed. This method first proposes that on the Mask branch, Convolution Condition Random Field(ConvCRF) is used to optimize the Mask branch, and the candidate area is further segmented, and uses FCN-ConvCRF branch to replace the original branch. Then, a new anchor size and IOU standard are proposed to enable the RPN candidate box cover all the instance areas. Finally, a training method is used to add a part of data transformed by the transformation network. Compared with the original algorithm, the total mAP value is improved by 3%, and the accuracy and robustness of segmentation boundary are improved to some extent.
- Image instance segmentation,
- Mask R-CNN,
- Conditional Random Field(CRF),
- RPN level

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References(25)

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