Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot Target
With the development of sonar technology, sonar images have been widely used to detect targets. However, there are many challenges for sonar images in terms of object detection. For example, the detectable targets in the sonar data are more sparse than those in optical images, the real underwater sc...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/24/6260 |
| _version_ | 1797455437661995008 |
|---|---|
| author | Jier Xi Xiufen Ye Chuanlong Li |
| author_facet | Jier Xi Xiufen Ye Chuanlong Li |
| author_sort | Jier Xi |
| collection | DOAJ |
| description | With the development of sonar technology, sonar images have been widely used to detect targets. However, there are many challenges for sonar images in terms of object detection. For example, the detectable targets in the sonar data are more sparse than those in optical images, the real underwater scanning experiment is complicated, and the sonar image styles produced by different types of sonar equipment due to their different characteristics are inconsistent, which makes it difficult to use them for sonar object detection and recognition algorithms. In order to solve these problems, we propose a novel sonar image object-detection method based on style learning and random noise with various shapes. Sonar style target sample images are generated through style transfer, which enhances insufficient sonar objects image. By introducing various noise shapes, which included points, lines, and rectangles, the problems of mud and sand obstruction and a mutilated target in the real environment are solved, and the single poses of the sonar image target is improved by fusing multiple poses of optical image target. In the meantime, a method of feature enhancement is proposed to solve the issue of missing key features when using style transfer on optical images directly. The experimental results show that our method achieves better precision. |
| first_indexed | 2024-03-09T15:54:24Z |
| format | Article |
| id | doaj.art-4d618c82aabd4f2fa3d2b155eef99517 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T15:54:24Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4d618c82aabd4f2fa3d2b155eef995172023-11-24T17:46:45ZengMDPI AGRemote Sensing2072-42922022-12-011424626010.3390/rs14246260Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot TargetJier Xi0Xiufen Ye1Chuanlong Li2College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaWith the development of sonar technology, sonar images have been widely used to detect targets. However, there are many challenges for sonar images in terms of object detection. For example, the detectable targets in the sonar data are more sparse than those in optical images, the real underwater scanning experiment is complicated, and the sonar image styles produced by different types of sonar equipment due to their different characteristics are inconsistent, which makes it difficult to use them for sonar object detection and recognition algorithms. In order to solve these problems, we propose a novel sonar image object-detection method based on style learning and random noise with various shapes. Sonar style target sample images are generated through style transfer, which enhances insufficient sonar objects image. By introducing various noise shapes, which included points, lines, and rectangles, the problems of mud and sand obstruction and a mutilated target in the real environment are solved, and the single poses of the sonar image target is improved by fusing multiple poses of optical image target. In the meantime, a method of feature enhancement is proposed to solve the issue of missing key features when using style transfer on optical images directly. The experimental results show that our method achieves better precision.https://www.mdpi.com/2072-4292/14/24/6260sonar imagestyle transferrandom shape of noisemultiple posesfeature enhancement |
| spellingShingle | Jier Xi Xiufen Ye Chuanlong Li Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot Target Remote Sensing sonar image style transfer random shape of noise multiple poses feature enhancement |
| title | Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot Target |
| title_full | Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot Target |
| title_fullStr | Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot Target |
| title_full_unstemmed | Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot Target |
| title_short | Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot Target |
| title_sort | sonar image target detection based on style transfer learning and random shape of noise under zero shot target |
| topic | sonar image style transfer random shape of noise multiple poses feature enhancement |
| url | https://www.mdpi.com/2072-4292/14/24/6260 |
| work_keys_str_mv | AT jierxi sonarimagetargetdetectionbasedonstyletransferlearningandrandomshapeofnoiseunderzeroshottarget AT xiufenye sonarimagetargetdetectionbasedonstyletransferlearningandrandomshapeofnoiseunderzeroshottarget AT chuanlongli sonarimagetargetdetectionbasedonstyletransferlearningandrandomshapeofnoiseunderzeroshottarget |