Method for the multi-view estimation of fish mass using a two-stage neural network with edge-sensitive module

The estimation of fish mass is one of the most basic and important tasks in aquaculture. Acquiring the mass of fish at different growth stages is of great significance for feeding, monitoring the health status of fish, and making breeding plans to increase production. The existing estimation methods for fish mass often stay in the 2D plane, and it is difficult to obtain the 3D information on fish, which will lead to the error. To solve this problem, a multi-view method was proposed to obtain the 3D information of fish and predict the mass of fish through a two-stage neural network with an edge-sensitive module. In the first stage, the side- and downward-view images of the fish and some 3D information, such as side area, top area, length, deflection angle, and pitch angle, were captured to estimate the size of the fish through two vertically placed cameras. Then the area of the fish at different views was estimated accurately through the pre-trained image segmentation neural network with an edge-sensitive module. In the second stage, a fully connected neural network was constructed to regress the fish mass based on the 3D information obtained in the previous stage. The experimental results indicate that the proposed method can accurately estimate the fish mass and outperform the existing estimation methods.