公开(公告)号：US12039733B2

公开(公告)日：2024-07-16

申请号：US17341852

申请日：2021-06-08

申请人： LUDONG UNIVERSITY

发明人： Jun Yue ,  Beibei Li ,  Shixiang Jia ,  Zhenbo Li ,  Qing Wang ,  Zheng Ma ,  Guangjie Kou ,  Tao Yao ,  Zhenzhong Li

IPC分类号： G06T7/11 ,  G06N3/04 ,  G06N3/086 ,  G06T7/10 ,  G06V10/25 ,  G06V10/82 ,  G06V40/10

CPC分类号： G06T7/11 ,  G06N3/04 ,  G06N3/086 ,  G06T7/10 ,  G06V10/25 ,  G06V10/82 ,  G06V40/10

摘要： 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.

公开(公告)号：US20210383149A1

公开(公告)日：2021-12-09

申请号：US17341852

申请日：2021-06-08

申请人： LUDONG UNIVERSITY

发明人： Jun Yue ,  Beibei Li ,  Shixiang Jia ,  Zhenbo Li ,  Qing Wang ,  Zheng Ma ,  Guangjie Kou ,  Tao Yao ,  Zhenzhong Li

IPC分类号： G06K9/32 ,  G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06T7/10

摘要： 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.