Problems faced by marine scientists during the assessment of Nephrops norvegicus species during underwater TV surveys have been addressed in this thesis. One of the main contributions of the work has been the study of the behavior of deep learning algorithms on the complex underwater dataset.
Currently, the Nephrops data are collected through the UWTV surveys and are reviewed manually by trained experts. Burrows systems are quantified following the protocol established by ICES.
Our first contribution is to develop the dataset for the deep learning models. No such dataset exists that someone can use to validate the results. After many revisions, the current work selected a few videos for annotation (the videos are selected with Marine experts based on the Nephrops burrows densities). The Marine expert validates each annotation before adding it to the dataset. After validating each annotation, a curated dataset is used for training and testing the model.
Different types of deep learning-based models have been finetuned and applied to the created dataset. The work proposed five different neural networks: MobileNet, Inception, ResNet50, ResNet101, and YOLOv3. All the models are trained and tested with the different combinations of datasets. A complete methodology is proposed for automatically detecting Nephrops burrows. The automatic detection algorithms could replace the human review of data, with the promise of better accuracy, coverage of more significant areas and higher consistency in the assessment.
Deep learning algorithms performed very well in identifying the burrows. Generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. This thesis contributes by developing a Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus burrows.