Salient Instance Segmentation via Multiscale Boundary Characteristic Network

Abstract

Locating objects of interest is a basic task in the application of computer vision. Salient instance segmentation can obtain instance of interest by detecting visually significant objects and segmenting them at pixel level. In order to utilize the ability of feature separation between target object and its surrounding background, single stage salient-instance segmentation (S4Net) designs a new region feature extraction layer called ROIMasking. For the characteristics of convolutional neural network, repeated convolution and upsampling will result in the loss of boundary information, rough boundary segmentation and the reduction of segmentation accuracy. To solve this problem, using the target edge detection method, a new end-to-end salient instance segmentation via multiscale boun-dary characteristic network (MBCNet) based on S4Net is proposed. This method designs a multiscale boundary feature extraction branch. A boundary refinement block with hybrid dilation convolution and residual network structure is used to enhance the extraction of the instance boundary information. The MBCNet sharing layers realize to transfer the boundary information. At the same time, in order to promote the accuracy of segmentation, a new boundary-segmentation joint loss function is proposed, realizing synchronous training of target boundary feature ext-raction and instance segmentation in the same network. Experimental results show that, compared with S4Net, the mAP0.5 and mAP0.7 of the proposal are 88.90% and 67.94% on the saliency instance dataset, with the improvement of 2.20 percentage points and 4.24 percentage points, respectively.

Keywords

• |salient instance segmentation|edge detection|boundary refinement|boundary-segmentation joint training