Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar
Imaging sonar is a primary means of underwater detection, but it faces challenges of high false alarm rates in sonar image target detection due to factors such as reverberation, noise, and resolution. This paper proposes a method to improve the false alarm rate by self-training a deep learning detec...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/3/1189 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850199450891321344 |
|---|---|
| author | Jingqi Han Yue Fan Zheng He Zhenhang You Peng Zhang Zhengliang Hu |
| author_facet | Jingqi Han Yue Fan Zheng He Zhenhang You Peng Zhang Zhengliang Hu |
| author_sort | Jingqi Han |
| collection | DOAJ |
| description | Imaging sonar is a primary means of underwater detection, but it faces challenges of high false alarm rates in sonar image target detection due to factors such as reverberation, noise, and resolution. This paper proposes a method to improve the false alarm rate by self-training a deep learning detector on sonar images. Self-training automatically generates proxy classification tasks based on the sonar image target detection dataset, and pre-trains the deep learning detector through these proxy classification tasks to enhance its learning effectiveness of target and background features. This, in turn, improves the detector’s ability to distinguish between targets and backgrounds, thereby reducing the false alarm rate. For the first time, this paper conducts target detection experiments based on deep learning using high-resolution synthetic aperture sonar images at two frequencies. The results show that, under the conditions of equal or higher recall rates, this method can reduce the false alarm rate by 3.91% and 18.50% on 240 kHz and 450 kHz sonar images, respectively, compared to traditional transfer learning methods. |
| format | Article |
| id | doaj-art-ea81daa853904d3495a9b86fd3866f8a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-ea81daa853904d3495a9b86fd3866f8a2025-08-20T02:12:37ZengMDPI AGApplied Sciences2076-34172025-01-01153118910.3390/app15031189Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging SonarJingqi Han0Yue Fan1Zheng He2Zhenhang You3Peng Zhang4Zhengliang Hu5College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, ChinaImaging sonar is a primary means of underwater detection, but it faces challenges of high false alarm rates in sonar image target detection due to factors such as reverberation, noise, and resolution. This paper proposes a method to improve the false alarm rate by self-training a deep learning detector on sonar images. Self-training automatically generates proxy classification tasks based on the sonar image target detection dataset, and pre-trains the deep learning detector through these proxy classification tasks to enhance its learning effectiveness of target and background features. This, in turn, improves the detector’s ability to distinguish between targets and backgrounds, thereby reducing the false alarm rate. For the first time, this paper conducts target detection experiments based on deep learning using high-resolution synthetic aperture sonar images at two frequencies. The results show that, under the conditions of equal or higher recall rates, this method can reduce the false alarm rate by 3.91% and 18.50% on 240 kHz and 450 kHz sonar images, respectively, compared to traditional transfer learning methods.https://www.mdpi.com/2076-3417/15/3/1189underwater target detectionsonar imagedeep learning |
| spellingShingle | Jingqi Han Yue Fan Zheng He Zhenhang You Peng Zhang Zhengliang Hu Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar Applied Sciences underwater target detection sonar image deep learning |
| title | Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar |
| title_full | Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar |
| title_fullStr | Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar |
| title_full_unstemmed | Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar |
| title_short | Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar |
| title_sort | self training can reduce detection false alarm rate of high resolution imaging sonar |
| topic | underwater target detection sonar image deep learning |
| url | https://www.mdpi.com/2076-3417/15/3/1189 |
| work_keys_str_mv | AT jingqihan selftrainingcanreducedetectionfalsealarmrateofhighresolutionimagingsonar AT yuefan selftrainingcanreducedetectionfalsealarmrateofhighresolutionimagingsonar AT zhenghe selftrainingcanreducedetectionfalsealarmrateofhighresolutionimagingsonar AT zhenhangyou selftrainingcanreducedetectionfalsealarmrateofhighresolutionimagingsonar AT pengzhang selftrainingcanreducedetectionfalsealarmrateofhighresolutionimagingsonar AT zhenglianghu selftrainingcanreducedetectionfalsealarmrateofhighresolutionimagingsonar |