Advancing Automated Detection of Nephrops norvegicus Burrows in Underwater Television Surveys through Machine Learning
The paper introduces computer vision methods for automating the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. This approach aims to improve accuracy, reduce human errors, and standardize the current manual video analysis process. By using machine learning techniques, the system can automatically process video streams and detect N. norvegicus burrow openings on the seabed. The work also explores the use of data augmentation algorithms to extend the annotated data set, enhancing the performance of the automated system compared to the original manual annotations.
Authors
- Gabriele Pieri (ORCID: https://orcid.org/0000-0001-5068-2861)
- Marco Reggiannini (ORCID: https://orcid.org/0000-0002-4872-9541)
- Lorenzo Zacchetti (ORCID: https://orcid.org/0000-0003-3179-7473)
- Filippo Domenichetti (ORCID: https://orcid.org/0000-0002-8052-7544)
- Oscar Papini (ORCID: https://orcid.org/0000-0003-2069-5068)
- Michela Martinelli (ORCID: https://orcid.org/0000-0003-1231-2346)
- Enrico Cecapolli (ORCID: https://orcid.org/0009-0009-7996-1691)
Institutions
- Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" (IT)
- National Research Council (IT)
- University of Bologna (IT)
Publication Details
- Journal
- Pattern Recognition and Image Analysis
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1134/s1054661824701062
- Citations
- 1
- Primary Topic
- Marine and fisheries research
- Type
- article
- Field-Weighted Citation Impact
- 0.28