Exploring the Incremental Improvements of YOLOv5 on Tracking and Identifying Great White Sharks in Cape Town
- Kuhlane, Luxolo L, Brown, Dane L, Boby, Alden
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Boby, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464107 , vital:76476 , 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.
- Full Text:
- Date Issued: 2023
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Boby, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464107 , vital:76476 , 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.
- Full Text:
- Date Issued: 2023
Plant disease detection using multispectral imaging
- De Silva, Malitha, Brown, Dane L
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463439 , vital:76409 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35641-4_24"
- Description: People worldwide are undergoing many challenges, including food scarcity. Many pieces of research are now focused on improving agriculture to increase the harvest and reduce the cost. Identifying plant diseases and pests in the early stages helps to enhance the yield and reduce costs. However, most plant disease identification research with computer vision has been done with images taken in controlled environments on publically available data sets. Near-Infrared (NIR) imaging is a favourable approach for identifying plant diseases. Therefore, this study collected NIR images of healthy and diseased leaves in the natural environment. The dataset is tested with eight Convolutional Neural Network (CNN) models with different train-test splits ranging from 10:90 to 90:10. The evaluated models attained their highest training and test accuracies from the 70:30 split onwards. Xception outperformed all the other models in all train-test splits and achieved 100% accuracy, precision and recall in the 80:20 train-test split.
- Full Text:
- Date Issued: 2022
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463439 , vital:76409 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35641-4_24"
- Description: People worldwide are undergoing many challenges, including food scarcity. Many pieces of research are now focused on improving agriculture to increase the harvest and reduce the cost. Identifying plant diseases and pests in the early stages helps to enhance the yield and reduce costs. However, most plant disease identification research with computer vision has been done with images taken in controlled environments on publically available data sets. Near-Infrared (NIR) imaging is a favourable approach for identifying plant diseases. Therefore, this study collected NIR images of healthy and diseased leaves in the natural environment. The dataset is tested with eight Convolutional Neural Network (CNN) models with different train-test splits ranging from 10:90 to 90:10. The evaluated models attained their highest training and test accuracies from the 70:30 split onwards. Xception outperformed all the other models in all train-test splits and achieved 100% accuracy, precision and recall in the 80:20 train-test split.
- Full Text:
- Date Issued: 2022
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