- Title
- Comparative analysis of YOLOV5 and YOLOV8 for automated fish detection and classification in underwater environments
- Creator
- Kuhlane, Luxolo
- ThesisAdvisor
- Brown, D.L.
- Subject
- Uncatalogued
- Date
- 2024-10-11
- Type
- Academic theses
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10962/464333
- Identifier
- vital:76502
- Description
- The application of traditional manual techniques for fish detection and classification faces significant challenges, primarily stemming from their labour-intensive nature and limited scalability. Automating these kinds of processes through computer vision practices and machine learning techniques has emerged as a potential solution in recent years. With the development of and increase in ease of access to new technology in recent years, the use of a deep learning object detector known as YOLO (You Only Look Once) in the detection and classification of fish has steadily become notably popular. This thesis thus explores suitable YOLO architectures for detecting and classifying fish. The YOLOv5 and YOLOv8 models were evaluated explicitly for detecting and classifying fish in underwater environments. The selection of these models was based on a literature review highlighting their success in similar applications but remains largely understudied in underwater environments. Therefore, the effectiveness of these models was evaluated through comprehensive experimentation on collected and publicly available underwater fish datasets. In collaboration with the South African Institute of Biodiversity (SAIAB), five datasets were collected and manually annotated for labels for supervised machine learning. Moreover, two publicly available datasets were sourced for comparison to the literature. Furthermore, after determining that the smallest YOLO architectures are better suited to these imbalanced datasets, hyperparameter tuning tailored the models to the characteristics of the various underwater environments used in the research. The popular DeepFish dataset was evaluated to establish a baseline and feasibility of these models in the understudied domain. The results demonstrated high detection accuracy for both YOLOv5 and YOLOv8. However, YOLOv8 outperformed YOLOv5, achieving 97.43% accuracy compared to 94.53%. After experiments on seven datasets, trends revealed YOLOv8’s enhanced generalisation accuracy due to architectural improvements, particularly in detecting smaller fish. Overall, YOLOv8 demonstrated that it is the better fish detection and classification model on diverse data.
- Description
- Thesis (MSc) -- Faculty of Science, Computer Science, 2024
- Format
- computer, online resource, application/pdf, 1 online resource (135 pages), pdf
- Publisher
- Rhodes University, Faculty of Science, Computer Science
- Language
- English
- Rights
- Kuhlane, Luxolo
- Rights
- Use of this resource is governed by the terms and conditions of the Creative Commons "Attribution-NonCommercial-ShareAlike" License (http://creativecommons.org/licenses/by-nc-sa/2.0/)
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | KUHLANE-MSC-TR24-157.pdf | 1 MB | Adobe Acrobat PDF | View Details Download |