Segmentation and measurement scheme for fish morphological features based on Mask R-CNN

Abstract

The morphological features of fish, such as the body length, the body width, the caudal peduncle length, the caudal peduncle width, the pupil diameter, and the eye diameter are very important indicators in smart mariculture. Therefore, the accurate measurement of the morphological features is of great significance. However, the existing measurement methods mainly rely on manual measurement, which is operationally complex, low efficiency, and high subjectivity. To address these issues, this paper proposes a scheme for segmenting fish image and measuring fish morphological features indicators based on Mask R-CNN. Firstly, the fish body images are acquired by a home-made image acquisition device. Then, the fish images are preprocessed and labeled, and fed into the Mask R-CNN for training. Finally, the trained model is used to segment fish image, thus the morphological features indicators of the fish can be obtained. The experimental results demonstrate that the proposed scheme can segment the fish body in pure and complex backgrounds with remarkable performance. In pure background, the average relative errors (AREs) of all indicators measured all are less than 2.8%, and the AREs of body length and body width are less than 0.8%. In complex background, the AREs of all indicators are less than 3%, and the AREs of body length and body width is less than 1.8%.