基于改进Mask R-CNN的模糊图像实例分割的研究

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

Mask R-CNN是现阶段实例分割相对成熟的方法,针对Mask R-CNN算法当中还存在的分割边界精度以及对于模糊图片鲁棒性较差等问题,该文提出一种基于改进的Mask R-CNN实例分割方法。该方法首先提出在Mask分支上使用卷积化条件随机场(ConvCRF)来优化Mask分支对于候选区域进一步分割,并使用FCN-ConvCRF分支来代替原有分支;之后提出新锚点大小和IOU标准,使得RPN候选框能够涵盖所有实例区域;最后使用一种添加部分经过转换网络转换的数据进行训练的方法。总的mAP值与原算法相比提升了3%,并且分割边界精确度和鲁棒性都有一定提高。

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.