TITLE:
Underwater Inhomogeneous Light Field Based on Improved Convolutional Neural Net Fish Image Recognition
AUTHORS:
Kai Liu, Siyu Wang, Yadong Wu, Weihan Zhang
KEYWORDS:
Heterogeneous Light Field under Water, CNN, Image Recognition
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.13 No.7,
July
26,
2023
ABSTRACT: In this paper, artificial
intelligence image recognition technology is used to improve the recognition
rate of individual domestic fish and reduce the recognition time, aiming at the
problem that it is difficult to easily observe the species and growth of domestic
fish in the underwater non-uniform light field environment. First, starting
from the image data collected by polarizing imaging technology, this paper uses
subpixel convolution reconstruction to enhance the image, uses image
translation and fill technology to build the family fish database, builds the
Adam-Dropout-CNN (A-D-CNN) network model, and its convolution kernel size is 3
× 3. The maximum pooling was used for downsampling, and the discarding
operation was added after the full connection layer to avoid the phenomenon of
network overfitting. The adaptive motion estimation algorithm was used to solve
the gradient sparse problem. The experiment shows that the recognition rate of
A-D-CNN is 96.97% when the model is trained under the domestic fish image
database, which solves the problem of low recognition rate and slow recognition
speed of domestic fish in non-uniform light field.