A method for identifying an individual of an oplegnathus punctatus based on a convolutional neural network is provided. Target initial positioning involves three continuous convolutional layers and an average pooling layer. A region of feature interest point is obtained, a hyperparameter candidate box is set to obtain a region, thereby obtaining an approximate position of a target object. II_Net backbone convolutional neural network includes six convolutional layers and four pooling layers, which includes a LeakyReLU activation function used as an activation function of the first convolutional layer, convolutional network layers of Alexnet and parameter data, and a maximum pooling layer of an overlapped pooling structure. Fully connected layers use a genetic algorithm to improve data transmission between layers. With the established model, identification of the individual of the oplegnathus punctatus is performed by using test data.
METHOD FOR IDENTIFYING INDIVIDUALS OF OPLEGNATHUS PUNCTATUS BASED ON CONVOLUTIONAL NEURAL NETWORK