DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing Technique
Ship detection and identification is the key part of the maritime monitoring and safety. Ship monitoring methods based on coastal video surveillance, satellite imagery, and synthetic aperture radar have been well developed. As the emerging remote sensing technology, distributed acoustic sensing (DAS...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10820076/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576738615361536 |
|---|---|
| author | Wenjin Huang Shaoyi Chen Yichang Wu Ruihua Li Tianrui Li Yihua Huang Xiaochun Cao Zhaohui Li |
| author_facet | Wenjin Huang Shaoyi Chen Yichang Wu Ruihua Li Tianrui Li Yihua Huang Xiaochun Cao Zhaohui Li |
| author_sort | Wenjin Huang |
| collection | DOAJ |
| description | Ship detection and identification is the key part of the maritime monitoring and safety. Ship monitoring methods based on coastal video surveillance, satellite imagery, and synthetic aperture radar have been well developed. As the emerging remote sensing technology, distributed acoustic sensing (DAS) technology which continuously detects vibrations along underwater optical fiber cables facilitates all-weather, all-day, and real-time ship detection capabilities, possessing the potential for detecting dark ships. However, the reliance on expert knowledge for analyzing ship passage signals hinders the development of an automated framework for ship detection, limiting the application of DAS technology in the ship detection. In addition, the scarcity of datasets for ship passage events in the DAS field hampers the adoption of deep learning technologies for enhancing ship detection. To address these challenges, an automatic annotation method is proposed, utilizing 18 625 cleaned ship records based on the automatic identification system (AIS) to annotate ship passages adaptively from 5-month DAS data. Thus, a large-scale, high-quality annotated dataset named DAShip is established, containing 55 875 ship passage samples. Furthermore, an online ship detection and identification framework is proposed to achieve real-time ship detection from the massive DAS data flow and further identify coarse-grained ship features, such as ship speed, heading, angle, and ship type. In this proposed framework, YOLO models, primarily trained on DAShip, are used as ship detectors and ship feature classifiers, achieving accurate dark ship detection combined with AIS message and demonstrating competitive performance in ship feature classification. |
| format | Article |
| id | doaj-art-16ddb1bea8de41ecaf488a84bbbeabdd |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-16ddb1bea8de41ecaf488a84bbbeabdd2025-01-31T00:00:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184093410710.1109/JSTARS.2024.352508210820076DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing TechniqueWenjin Huang0https://orcid.org/0000-0002-8861-4263Shaoyi Chen1Yichang Wu2Ruihua Li3Tianrui Li4https://orcid.org/0000-0002-4789-6428Yihua Huang5https://orcid.org/0000-0001-6736-7913Xiaochun Cao6https://orcid.org/0000-0001-7141-708XZhaohui Li7https://orcid.org/0000-0002-6151-8642School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, ChinaSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, ChinaSchool of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University, Shenzhen, ChinaGuangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems, School of Electrical and Information Technology, Sun Yat-Sen University, Guangzhou, ChinaShip detection and identification is the key part of the maritime monitoring and safety. Ship monitoring methods based on coastal video surveillance, satellite imagery, and synthetic aperture radar have been well developed. As the emerging remote sensing technology, distributed acoustic sensing (DAS) technology which continuously detects vibrations along underwater optical fiber cables facilitates all-weather, all-day, and real-time ship detection capabilities, possessing the potential for detecting dark ships. However, the reliance on expert knowledge for analyzing ship passage signals hinders the development of an automated framework for ship detection, limiting the application of DAS technology in the ship detection. In addition, the scarcity of datasets for ship passage events in the DAS field hampers the adoption of deep learning technologies for enhancing ship detection. To address these challenges, an automatic annotation method is proposed, utilizing 18 625 cleaned ship records based on the automatic identification system (AIS) to annotate ship passages adaptively from 5-month DAS data. Thus, a large-scale, high-quality annotated dataset named DAShip is established, containing 55 875 ship passage samples. Furthermore, an online ship detection and identification framework is proposed to achieve real-time ship detection from the massive DAS data flow and further identify coarse-grained ship features, such as ship speed, heading, angle, and ship type. In this proposed framework, YOLO models, primarily trained on DAShip, are used as ship detectors and ship feature classifiers, achieving accurate dark ship detection combined with AIS message and demonstrating competitive performance in ship feature classification.https://ieeexplore.ieee.org/document/10820076/Dark shipdatasetdistributed acoustic sensing (DAS) |
| spellingShingle | Wenjin Huang Shaoyi Chen Yichang Wu Ruihua Li Tianrui Li Yihua Huang Xiaochun Cao Zhaohui Li DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing Technique IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Dark ship dataset distributed acoustic sensing (DAS) |
| title | DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing Technique |
| title_full | DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing Technique |
| title_fullStr | DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing Technique |
| title_full_unstemmed | DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing Technique |
| title_short | DAShip: A Large-Scale Annotated Dataset for Ship Detection Using Distributed Acoustic Sensing Technique |
| title_sort | daship a large scale annotated dataset for ship detection using distributed acoustic sensing technique |
| topic | Dark ship dataset distributed acoustic sensing (DAS) |
| url | https://ieeexplore.ieee.org/document/10820076/ |
| work_keys_str_mv | AT wenjinhuang dashipalargescaleannotateddatasetforshipdetectionusingdistributedacousticsensingtechnique AT shaoyichen dashipalargescaleannotateddatasetforshipdetectionusingdistributedacousticsensingtechnique AT yichangwu dashipalargescaleannotateddatasetforshipdetectionusingdistributedacousticsensingtechnique AT ruihuali dashipalargescaleannotateddatasetforshipdetectionusingdistributedacousticsensingtechnique AT tianruili dashipalargescaleannotateddatasetforshipdetectionusingdistributedacousticsensingtechnique AT yihuahuang dashipalargescaleannotateddatasetforshipdetectionusingdistributedacousticsensingtechnique AT xiaochuncao dashipalargescaleannotateddatasetforshipdetectionusingdistributedacousticsensingtechnique AT zhaohuili dashipalargescaleannotateddatasetforshipdetectionusingdistributedacousticsensingtechnique |