| Title | Underwater Small Target Recognition Based on Convolutional Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Li, Sichun, Jin, Xin, Yao, Sibing, Yang, Shuyu |
| Conference Name | Global Oceans 2020: Singapore – U.S. Gulf Coast |
| Date Published | Oct. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-5446-6 |
| Keywords | Acoustics, convolutional neural network, convolutional neural networks, Cyber physical system, Deep Learning, diver, dolphin, image recognition, Metrics, pubcrawl, resilience, Resiliency, Scalability, security, Target recognition, Task Analysis, Time-frequency Analysis, Underwater Networks, underwater small target recognition, whale, Whales |
| Abstract | With the increasingly extensive use of diver and unmanned underwater vehicle in military, it has posed a serious threat to the security of the national coastal area. In order to prevent the underwater diver's impact on the safety of water area, it is of great significance to identify underwater small targets in time to make early warning for it. In this paper, convolutional neural network is applied to underwater small target recognition. The recognition targets are diver, whale and dolphin. Due to the time-frequency spectrum can reflect the essential features of underwater target, convolutional neural network can learn a variety of features of the acoustic signal through the image processed by the time-frequency spectrum, time-frequency image is input to convolutional neural network to recognize the underwater small targets. According to the study of learning rate and pooling mode, the network parameters and structure suitable for underwater small target recognition in this paper are selected. The results of data processing show that the method can identify underwater small targets accurately. |
| URL | https://ieeexplore.ieee.org/document/9389160 |
| DOI | 10.1109/IEEECONF38699.2020.9389160 |
| Citation Key | li_underwater_2020 |
Underwater Small Target Recognition Based on Convolutional Neural Network