Side-scan sonar imaging data of underwater vehicles for mine detection
Unmanned vehicles have become increasingly popular in the underwater domain in the last decade, as they provide better operation reliability by minimizing human involvement in most tasks. Perception of the environment is crucial for safety and other tasks, such as guidance and trajectory control, ma...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924001045 |
| _version_ | 1797256383053168640 |
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| author | Nuno Pessanha Santos Ricardo Moura Gonçalo Sampaio Torgal Victor Lobo Miguel de Castro Neto |
| author_facet | Nuno Pessanha Santos Ricardo Moura Gonçalo Sampaio Torgal Victor Lobo Miguel de Castro Neto |
| author_sort | Nuno Pessanha Santos |
| collection | DOAJ |
| description | Unmanned vehicles have become increasingly popular in the underwater domain in the last decade, as they provide better operation reliability by minimizing human involvement in most tasks. Perception of the environment is crucial for safety and other tasks, such as guidance and trajectory control, mainly when operating underwater. Mine detection is one of the riskiest operations since it involves systems that can easily damage vehicles and endanger human lives if manned. Automating mine detection from side-scan sonar images enhances safety while reducing false negatives. The collected dataset contains 1170 real sonar images taken between 2010 and 2021 using a Teledyne Marine Gavia Autonomous Underwater Vehicle (AUV), which includes enough information to classify its content objects as NOn-Mine-like BOttom Objects (NOMBO) and MIne-Like COntacts (MILCO). The dataset is annotated and can be quickly deployed for object detection, classification, or image segmentation tasks. Collecting a dataset of this type requires a significant amount of time and cost, which increases its rarity and relevance to research and industrial development. |
| first_indexed | 2024-03-08T00:13:37Z |
| format | Article |
| id | doaj.art-f3520b19b5d245698fc501ed5e440877 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-24T22:20:52Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-f3520b19b5d245698fc501ed5e4408772024-03-20T06:09:48ZengElsevierData in Brief2352-34092024-04-0153110132Side-scan sonar imaging data of underwater vehicles for mine detectionNuno Pessanha Santos0Ricardo Moura1Gonçalo Sampaio Torgal2Victor Lobo3Miguel de Castro Neto4Portuguese Military Research Center (CINAMIL), Portuguese Military Academy (Academia Militar), Lisbon 1169-203, Portugal; Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST), Lisbon 1049-001, Portugal; Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada 2810-001, Portugal; Corresponding author at: Portuguese Military Research Center (CINAMIL), Portuguese Military Academy (Academia Militar), Rua Gomes Freire, Lisbon 1169-203, Portugal.Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada 2810-001, Portugal; Centro de Matemática e Aplicações (Nova Math), Universidade Nova de Lisboa, Caparica 2829-516, PortugalPortuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada 2810-001, PortugalPortuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada 2810-001, Portugal; NOVA Information Management School (Nova IMS), Universidade Nova de Lisboa, Lisbon 1070-312, PortugalNOVA Information Management School (Nova IMS), Universidade Nova de Lisboa, Lisbon 1070-312, PortugalUnmanned vehicles have become increasingly popular in the underwater domain in the last decade, as they provide better operation reliability by minimizing human involvement in most tasks. Perception of the environment is crucial for safety and other tasks, such as guidance and trajectory control, mainly when operating underwater. Mine detection is one of the riskiest operations since it involves systems that can easily damage vehicles and endanger human lives if manned. Automating mine detection from side-scan sonar images enhances safety while reducing false negatives. The collected dataset contains 1170 real sonar images taken between 2010 and 2021 using a Teledyne Marine Gavia Autonomous Underwater Vehicle (AUV), which includes enough information to classify its content objects as NOn-Mine-like BOttom Objects (NOMBO) and MIne-Like COntacts (MILCO). The dataset is annotated and can be quickly deployed for object detection, classification, or image segmentation tasks. Collecting a dataset of this type requires a significant amount of time and cost, which increases its rarity and relevance to research and industrial development.http://www.sciencedirect.com/science/article/pii/S2352340924001045Autonomous underwater vehiclesUnmanned underwater vehiclesSonar measurementsSonar detectionSide-scan sonar |
| spellingShingle | Nuno Pessanha Santos Ricardo Moura Gonçalo Sampaio Torgal Victor Lobo Miguel de Castro Neto Side-scan sonar imaging data of underwater vehicles for mine detection Data in Brief Autonomous underwater vehicles Unmanned underwater vehicles Sonar measurements Sonar detection Side-scan sonar |
| title | Side-scan sonar imaging data of underwater vehicles for mine detection |
| title_full | Side-scan sonar imaging data of underwater vehicles for mine detection |
| title_fullStr | Side-scan sonar imaging data of underwater vehicles for mine detection |
| title_full_unstemmed | Side-scan sonar imaging data of underwater vehicles for mine detection |
| title_short | Side-scan sonar imaging data of underwater vehicles for mine detection |
| title_sort | side scan sonar imaging data of underwater vehicles for mine detection |
| topic | Autonomous underwater vehicles Unmanned underwater vehicles Sonar measurements Sonar detection Side-scan sonar |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924001045 |
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