Underwater Image Fish Recognition Technology Based on Transfer Learning and Image Enhancement

To effectively identify fish targets in underwater images, an image processing technology based on secondary migration learning and the Retinex algorithm is proposed to solve the problem of few underwater data sets and unclear underwater images. This method only uses a small-scale underwater image data set to train the network, and the Faster regionbased convolutional neural networks (R-CNN) model can quickly detect underwater fish targets. The first transfer learning is applied between the ultra-large-scale ImageNet open-source dataset and the medium-scale Open Images high-definition fish dataset. Then, the Retinex iterative algorithm is used to enhance the underwater image to apply the second transfer learning between the high-resolution medium-scale fish data set and the small-scale underwater data set. Experiments show that this method can train a very effective detection model using small underwater image data sets at a low cost. The effect and performance of the detection model are far superior to traditional machine learning methods. This research can provide a certain reference value for deep-sea exploration, resource protection, and other engineering applications.