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

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.