Exploring The Incremental Improvements of YOLOv7 on Bull Sharks in Mozambique
- Kuhlane, Luxolo L, Brown, Dane L, Brown, Alden
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Brown, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464118 , vital:76478 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/368455814_Exploring_The_Incremental_Improvements_of_YOLOv7_on_Bull_Sharks_in_Mozambique/links/63e8d321dea6121757a4ba7f/Exploring-The-Incremental-Improvements-of-YOLOv7-on-Bull-Sharks-in-Mozambique.pdf?origin=journalDetailand_tp=eyJwYWdlIjoiam91cm5hbERldGFpbCJ9"
- Description: Scientists use bull shark data to better understand marine organisms and to reduce the likelihood of bull shark extinction. Sharks play an important role in the ocean, and their importance is underappreciated by the general public, leading to negative attitudes toward sharks. The tracking and identification of sharks is done by hand, which is inefficient and time-consuming. This paper employs a deep learning approach to assist in the identification and tracking of bull sharks in Mozambique. YOLO is a popular object detection system used in this paper to aid in the identification of the great white shark. In addition to YOLO, the paper employs ESRGAN to help upscale low-quality images from the datasets into higher-quality images before they are fed into the YOLO system. The primary goal of this paper is to assist in training the system to identify bull 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 , Brown, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/464118 , vital:76478 , xlink:href="https://www.researchgate.net/profile/Dane-Brown-2/publication/368455814_Exploring_The_Incremental_Improvements_of_YOLOv7_on_Bull_Sharks_in_Mozambique/links/63e8d321dea6121757a4ba7f/Exploring-The-Incremental-Improvements-of-YOLOv7-on-Bull-Sharks-in-Mozambique.pdf?origin=journalDetailand_tp=eyJwYWdlIjoiam91cm5hbERldGFpbCJ9"
- Description: Scientists use bull shark data to better understand marine organisms and to reduce the likelihood of bull shark extinction. Sharks play an important role in the ocean, and their importance is underappreciated by the general public, leading to negative attitudes toward sharks. The tracking and identification of sharks is done by hand, which is inefficient and time-consuming. This paper employs a deep learning approach to assist in the identification and tracking of bull sharks in Mozambique. YOLO is a popular object detection system used in this paper to aid in the identification of the great white shark. In addition to YOLO, the paper employs ESRGAN to help upscale low-quality images from the datasets into higher-quality images before they are fed into the YOLO system. The primary goal of this paper is to assist in training the system to identify bull sharks in difficult conditions such as murky water or unclear deep-sea conditions.
- Full Text:
- Date Issued: 2023
Exploring the Incremental Improvements of YOLOv7 over YOLOv5 for Character Recognition
- Boby, Alden, Brown, Dane L, Connan, James, Marais, Marc
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463395 , vital:76405 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35644-5_5"
- Description: Technological advances are being applied to aspects of life to improve quality of living and efficiency. This speaks specifically to automation, especially in the industry. The growing number of vehicles on the road has presented a need to monitor more vehicles than ever to enforce traffic rules. One way to identify a vehicle is through its licence plate, which contains a unique string of characters that make it identifiable within an external database. Detecting characters on a licence plate using an object detector has only recently been explored. This paper uses the latest versions of the YOLO object detector to perform character recognition on licence plate images. This paper expands upon existing object detection-based character recognition by investigating how improvements in the framework translate to licence plate character recognition accuracy compared to character recognition based on older architectures. Results from this paper indicate that the newer YOLO models have increased performance over older YOLO-based character recognition models such as CRNET.
- Full Text:
- Date Issued: 2022
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463395 , vital:76405 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35644-5_5"
- Description: Technological advances are being applied to aspects of life to improve quality of living and efficiency. This speaks specifically to automation, especially in the industry. The growing number of vehicles on the road has presented a need to monitor more vehicles than ever to enforce traffic rules. One way to identify a vehicle is through its licence plate, which contains a unique string of characters that make it identifiable within an external database. Detecting characters on a licence plate using an object detector has only recently been explored. This paper uses the latest versions of the YOLO object detector to perform character recognition on licence plate images. This paper expands upon existing object detection-based character recognition by investigating how improvements in the framework translate to licence plate character recognition accuracy compared to character recognition based on older architectures. Results from this paper indicate that the newer YOLO models have increased performance over older YOLO-based character recognition models such as CRNET.
- Full Text:
- Date Issued: 2022
A Robust Portable Environment for First-Year Computer Science Students
- Brown, Dane L, Connan, James
- Authors: Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465113 , vital:76574 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_6"
- Description: Computer science education in both South African universities and worldwide often aim at making students confident at problem solving by introducing various programming exercises. Standardising a computer environment where students can apply their computational thinking knowledge on a more even playing field – without worrying about software issues – can be beneficial for problem solving in classroom of diverse students. Research shows that having consistent access to this exposes students to core concepts of Computer Science. However, with the diverse student base in South Africa, not everyone has access to a personal computer or expensive software. This paper describes a new approach at first-year level that uses the power of a modified Linux distro on a flash drive to enable access to the same, fully-fledged, free and open-source environment, including the convenience of portability. This is used as a means to even the playing field in a diverse country like South Africa and address the lack of consistent access to a problem solving environment. Feedback from students and staff at the Institution are effectively heeded and attempted to be measured.
- Full Text:
- Date Issued: 2021
- Authors: Brown, Dane L , Connan, James
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465113 , vital:76574 , xlink:href="https://link.springer.com/chapter/10.1007/978-3-030-92858-2_6"
- Description: Computer science education in both South African universities and worldwide often aim at making students confident at problem solving by introducing various programming exercises. Standardising a computer environment where students can apply their computational thinking knowledge on a more even playing field – without worrying about software issues – can be beneficial for problem solving in classroom of diverse students. Research shows that having consistent access to this exposes students to core concepts of Computer Science. However, with the diverse student base in South Africa, not everyone has access to a personal computer or expensive software. This paper describes a new approach at first-year level that uses the power of a modified Linux distro on a flash drive to enable access to the same, fully-fledged, free and open-source environment, including the convenience of portability. This is used as a means to even the playing field in a diverse country like South Africa and address the lack of consistent access to a problem solving environment. Feedback from students and staff at the Institution are effectively heeded and attempted to be measured.
- Full Text:
- Date Issued: 2021
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