Counting of Underwater Static Objects Through an Efficient Temporal Technique
Counting marine species is a challenging task for biologists and marine experts. This paper presents an efficient temporal technique for counting underwater static objects. The proposed method employs deep learning techniques to detect objects over time and an efficient spatial–temporal algorithm to...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/2/205 |
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| Summary: | Counting marine species is a challenging task for biologists and marine experts. This paper presents an efficient temporal technique for counting underwater static objects. The proposed method employs deep learning techniques to detect objects over time and an efficient spatial–temporal algorithm to track them, allowing for accurate counting of objects within a given area. The technique is designed to handle various challenges that arise in underwater environments such as low visibility, object occlusion, and water turbulence. The approach is validated through experiments conducted on the surveyed data of <i>Nephrops</i> norvegicus. <i>Nephrops</i> is considered one of the main commercial species in Europe. <i>Nephrops</i> spend most of their time inside the burrows. Burrows tracking and counting are the only ways to monitor this species. This paper proposed a technique to accurately count underwater static objects using their spatial–temporal values by minimizing false positives. The proposed technique has potential applications in marine biology, environmental monitoring, and underwater surveillance. |
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| ISSN: | 2077-1312 |