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This paper presents an analysis of computer vision methods designed to automate the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. The proposed approach seeks to evaluate the accuracy, minimise human error, and standardise the existing manual video analysis process. By leveraging machine learning techniques, the system described in this paper autonomously processes video streams and identifies N. norvegicus burrow openings on the seabed. Additionally, this study investigates data augmentation algorithms to expand an annotated dataset and evaluates the performances of the first results under different configurations.

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Machine Learning Approaches for Automated Detection of Nephrops norvegicus Burrows in Underwater Surveys

  • 会议论文

  • 发表日期：

    2025年

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  • Oscar Papini,
  • Enrico Cecapolli,
  • Filippo Domenichetti,
  • Michela Martinelli,
  • Gabriele Pieri,
  • Marco Reggiannini,
  • Lorenzo Zacchetti

摘要

This paper presents an analysis of computer vision methods designed to automate the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. The proposed approach seeks to evaluate the accuracy, minimise human error, and standardise the existing manual video analysis process. By leveraging machine learning techniques, the system described in this paper autonomously processes video streams and identifies N. norvegicus burrow openings on the seabed. Additionally, this study investigates data augmentation algorithms to expand an annotated dataset and evaluates the performances of the first results under different configurations.