Method for identifying individuals of oplegnathus punctatus based on convolutional neural network
US12039733B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 8, 2021 |
| Grant date | Jul 16, 2024 |
| Priority date | — |
| Expiry date | Sep 26, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.