Enabling Vehicle Search Through Robust Licence Plate Detection
- Boby, Alden, Brown, Dane L, Connan, James, Marais, Marc, Kuhlane, Luxolo L
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc , Kuhlane, Luxolo L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463372 , vital:76403 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220508"
- Description: Licence plate recognition has many practical applications for security and surveillance. This paper presents a robust licence plate detection system that uses string-matching algorithms to identify a vehicle in data. Object detection models have had limited application in the character recognition domain. The system utilises the YOLO object detection model to perform character recognition to ensure more accurate character predictions. The model incorporates super-resolution techniques to enhance the quality of licence plate images to increase character recognition accuracy. The proposed system can accurately detect license plates in diverse conditions and can handle license plates with varying fonts and backgrounds. The system's effectiveness is demonstrated through experimentation on components of the system, showing promising license plate detection and character recognition accuracy. The overall system works with all the components to track vehicles by matching a target string with detected licence plates in a scene. The system has potential applications in law enforcement, traffic management, and parking systems and can significantly advance surveillance and security through automation.
- Full Text:
- Date Issued: 2023
- Authors: Boby, Alden , Brown, Dane L , Connan, James , Marais, Marc , Kuhlane, Luxolo L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463372 , vital:76403 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220508"
- Description: Licence plate recognition has many practical applications for security and surveillance. This paper presents a robust licence plate detection system that uses string-matching algorithms to identify a vehicle in data. Object detection models have had limited application in the character recognition domain. The system utilises the YOLO object detection model to perform character recognition to ensure more accurate character predictions. The model incorporates super-resolution techniques to enhance the quality of licence plate images to increase character recognition accuracy. The proposed system can accurately detect license plates in diverse conditions and can handle license plates with varying fonts and backgrounds. The system's effectiveness is demonstrated through experimentation on components of the system, showing promising license plate detection and character recognition accuracy. The overall system works with all the components to track vehicles by matching a target string with detected licence plates in a scene. The system has potential applications in law enforcement, traffic management, and parking systems and can significantly advance surveillance and security through automation.
- Full Text:
- Date Issued: 2023
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
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
Real-Time Detecting and Tracking of Squids Using YOLOv5
- Kuhlane, Luxolo L, Brown, Dane L, Marais, Marc
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Marais, Marc
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463467 , vital:76411 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220521"
- Description: This paper proposes a real-time system for detecting and tracking squids using the YOLOv5 object detection algorithm. The system utilizes a large dataset of annotated squid images and videos to train a YOLOv5 model optimized for detecting and tracking squids. The model is fine-tuned to minimize false positives and optimize detection accuracy. The system is deployed on a GPU-enabled device for real-time processing of video streams and tracking of detected squids across frames. The accuracy and speed of the system make it a valuable tool for marine scientists, conservationists, and fishermen to better understand the behavior and distribution of these elusive creatures. Future work includes incorporating additional computer vision techniques and sensor data to improve tracking accuracy and robustness.
- Full Text:
- Date Issued: 2023
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Marais, Marc
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463467 , vital:76411 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220521"
- Description: This paper proposes a real-time system for detecting and tracking squids using the YOLOv5 object detection algorithm. The system utilizes a large dataset of annotated squid images and videos to train a YOLOv5 model optimized for detecting and tracking squids. The model is fine-tuned to minimize false positives and optimize detection accuracy. The system is deployed on a GPU-enabled device for real-time processing of video streams and tracking of detected squids across frames. The accuracy and speed of the system make it a valuable tool for marine scientists, conservationists, and fishermen to better understand the behavior and distribution of these elusive creatures. Future work includes incorporating additional computer vision techniques and sensor data to improve tracking accuracy and robustness.
- Full Text:
- Date Issued: 2023
Deep Learning Approach to Image Deblurring and Image Super-Resolution using DeblurGAN and SRGAN
- Kuhlane, Luxolo L, Brown, Dane L, Connan, James, Boby, Alden, Marais, Marc
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Connan, James , Boby, Alden , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465157 , vital:76578 , xlink:href="https://www.researchgate.net/profile/Luxolo-Kuhlane/publication/363257796_Deep_Learning_Approach_to_Image_Deblurring_and_Image_Super-Resolution_using_DeblurGAN_and_SRGAN/links/6313b5a01ddd44702131b3df/Deep-Learning-Approach-to-Image-Deblurring-and-Image-Super-Resolution-using-DeblurGAN-and-SRGAN.pdf"
- Description: Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur of an image. Image deblurring and super-resolution, as representative image restoration problems, have been studied for a decade. Due to their wide range of applications, numerous techniques have been proposed to tackle these problems, inspiring innovations for better performance. Deep learning has become a robust framework for many image processing tasks, including restoration. In particular, generative adversarial networks (GANs), proposed by [1], have demonstrated remarkable performances in generating plausible images. However, training GANs for image restoration is a non-trivial task. This research investigates optimization schemes for GANs that improve image quality by providing meaningful training objective functions. In this paper we use a DeblurGAN and Super-Resolution Generative Adversarial Network (SRGAN) on the chosen dataset.
- Full Text:
- Date Issued: 2022
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Connan, James , Boby, Alden , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465157 , vital:76578 , xlink:href="https://www.researchgate.net/profile/Luxolo-Kuhlane/publication/363257796_Deep_Learning_Approach_to_Image_Deblurring_and_Image_Super-Resolution_using_DeblurGAN_and_SRGAN/links/6313b5a01ddd44702131b3df/Deep-Learning-Approach-to-Image-Deblurring-and-Image-Super-Resolution-using-DeblurGAN-and-SRGAN.pdf"
- Description: Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur of an image. Image deblurring and super-resolution, as representative image restoration problems, have been studied for a decade. Due to their wide range of applications, numerous techniques have been proposed to tackle these problems, inspiring innovations for better performance. Deep learning has become a robust framework for many image processing tasks, including restoration. In particular, generative adversarial networks (GANs), proposed by [1], have demonstrated remarkable performances in generating plausible images. However, training GANs for image restoration is a non-trivial task. This research investigates optimization schemes for GANs that improve image quality by providing meaningful training objective functions. In this paper we use a DeblurGAN and Super-Resolution Generative Adversarial Network (SRGAN) on the chosen dataset.
- Full Text:
- Date Issued: 2022
Investigating signer-independent sign language recognition on the lsa64 dataset
- Marais, Marc, Brown, Dane L, Connan, James, Boby, Alden, Kuhlane, Luxolo L
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden , Kuhlane, Luxolo L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465179 , vital:76580 , xlink:href="https://www.researchgate.net/profile/Marc-Marais/publication/363174384_Investigating_Signer-Independ-ent_Sign_Language_Recognition_on_the_LSA64_Dataset/links/63108c7d5eed5e4bd138680f/Investigating-Signer-Independent-Sign-Language-Recognition-on-the-LSA64-Dataset.pdf"
- Description: Conversing with hearing disabled people is a significant challenge; however, computer vision advancements have significantly improved this through automated sign language recognition. One of the common issues in sign language recognition is signer-dependence, where variations arise from varying signers, who gesticulate naturally. Utilising the LSA64 dataset, a small scale Argentinian isolated sign language recognition, we investigate signer-independent sign language recognition. An InceptionV3-GRU architecture is employed to extract and classify spatial and temporal information for automated sign language recognition. The signer-dependent approach yielded an accuracy of 97.03%, whereas the signer-independent approach achieved an accuracy of 74.22%. The signer-independent system shows promise towards addressing the real-world and common issue of signer-dependence in sign language recognition.
- Full Text:
- Date Issued: 2022
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden , Kuhlane, Luxolo L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465179 , vital:76580 , xlink:href="https://www.researchgate.net/profile/Marc-Marais/publication/363174384_Investigating_Signer-Independ-ent_Sign_Language_Recognition_on_the_LSA64_Dataset/links/63108c7d5eed5e4bd138680f/Investigating-Signer-Independent-Sign-Language-Recognition-on-the-LSA64-Dataset.pdf"
- Description: Conversing with hearing disabled people is a significant challenge; however, computer vision advancements have significantly improved this through automated sign language recognition. One of the common issues in sign language recognition is signer-dependence, where variations arise from varying signers, who gesticulate naturally. Utilising the LSA64 dataset, a small scale Argentinian isolated sign language recognition, we investigate signer-independent sign language recognition. An InceptionV3-GRU architecture is employed to extract and classify spatial and temporal information for automated sign language recognition. The signer-dependent approach yielded an accuracy of 97.03%, whereas the signer-independent approach achieved an accuracy of 74.22%. The signer-independent system shows promise towards addressing the real-world and common issue of signer-dependence in sign language recognition.
- Full Text:
- Date Issued: 2022
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