Standardization of Sonar Images by Conditional Random Fields for Fish Segmentation With Mask R-CNN

Abstract

Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. A preprocessing convolutional neural network (PreCNN) is proposed to provide "standardized" feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.