Whiteleg shrimp accounts for the highest proportion in the shrimp export of Vietnam. Yet, in hatcheries, shrimp larvae quantity is still estimated manually. Several approaches were proposed to address this issue but overlapping problem reduced accuracy significantly. In this paper, this problem is addressed by implementing two-phase Mask R-CNN based instance segmentation to segment shrimp larvae for counting purpose. Compared to one-phase Mask R-CNN, the accuracy of counting by applying two-phase Mask R-CNN increased by a maximum margin of 16.1%. Our model had remarkable results, with accuracy ranging from 92.2% to 95.4% for moderate overlapping images.