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.