- Title
- Exploring the Incremental Improvements of YOLOv5 on Tracking and Identifying Great White Sharks in Cape Town
- Creator
- Kuhlane, Luxolo L
- Creator
- Brown, Dane L
- Creator
- Boby, Alden
- Subject
- To be catalogued
- Date Issued
- 2023
- Date
- 2023
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/464107
- Identifier
- vital:76476
- Identifier
- xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-37963-5_98"
- Description
- The information on great white sharks is used by scientists to help better understand the marine organisms and to mitigate any chances of extinction of great white sharks. Sharks play a very important role in the ocean, and their role in the oceans is under-appreciated by the general public, which results in negative attitudes towards sharks. The tracking and identification of sharks are done using manual labour, which is not very accurate and time-consuming. This paper uses a deep learning approach to help identify and track great white sharks in Cape Town. A popular object detecting system used in this paper is YOLO, which is implemented to help identify the great white shark. In conjunction with YOLO, the paper also uses ESRGAN to help upscale low-quality images from the datasets into more high-quality images before being put into the YOLO system. The main focus of this paper is to help train the system; this includes training the system to identify great white sharks in difficult conditions such as murky water or unclear deep-sea conditions.
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (18 pages)
- Format
- Publisher
- SpringerLink
- Language
- English
- Relation
- Science and Information Conference
- Relation
- Kuhlane, L., Brown, D. and Boby, A., 2023, July. Exploring the Incremental Improvements of YOLOv5 on Tracking and Identifying Great White Sharks in Cape Town. In Science and Information Conference (pp. 1455-1473). Cham: Springer Nature Switzerland
- Relation
- Science and Information Conference p. 1455 2023 2367-3389
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
- Rights
- Closed Access
- Hits: 40
- Visitors: 40
- Downloads: 0
Collections
Brown, Dane A (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
SDG 14. Life Below Water
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Exploring the Incremental Improvements of YOLOv5 on Tracking and Identifying Great White Sharks in Cape Town.pdf | 666 KB | Adobe Acrobat PDF | View Details Download |