Comparative analysis of YOLOV5 and YOLOV8 for automated fish detection and classification in underwater environments
- Authors: Kuhlane, Luxolo
- Date: 2024-10-11
- Subjects: Artificial intelligence , Deep learning (Machine learning) , Machine learning , Neural networks (Computer science) , You Only Look Once , YOLOv5 , YOLOv8
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/464333 , 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. , Thesis (MSc) -- Faculty of Science, Computer Science, 2024
- Full Text:
- Authors: Kuhlane, Luxolo
- Date: 2024-10-11
- Subjects: Artificial intelligence , Deep learning (Machine learning) , Machine learning , Neural networks (Computer science) , You Only Look Once , YOLOv5 , YOLOv8
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/464333 , 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. , Thesis (MSc) -- Faculty of Science, Computer Science, 2024
- Full Text:
Enhancing licence plate recognition for a robust vehicle re-identification system
- Authors: Boby, Alden Zachary
- Date: 2024-10-11
- Subjects: Automobile theft South Africa , Deep learning (Machine learning) , Object detection , YOLOv7 , YOLO , Pattern recognition systems , Image processing Digital techniques , Automobile license plates
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/464322 , vital:76501
- Description: Vehicle security is a growing concern for citizens of South Africa. Law enforcement relies on reports and security camera footage for vehicle identification but struggles to match the increasing number of carjacking incidents and low vehicle recovery rates. Security camera footage offers an accessible means to identify stolen vehicles, yet it often poses hurdles like anamorphic plates and low resolution. Furthermore, depending on human operators proves inefficient, requiring faster processes to improve vehicle recovery rates and trust in law enforcement. The integration of deep learning has revolutionised object detection algorithms, increasing the popularity of vehicle tracking for security purposes. This thesis investigates advanced deep-learning methods for a comprehensive vehicle search and re-identification system. It enhances YOLOv7’s algorithmic capabilities and employs preprocessing techniques like super-resolution and perspective correction via the Improved Warped Planar Object Detection network for more effective licence plate optical character recognition. Key contributions include a specifically annotated dataset for training object detection models, an optical character recognition model based on YOLOv7, and a method for identifying vehicles in unrestricted data. The system detected rectangular and square licence plates without prior shape knowledge, achieving a 98.7% character recognition rate compared to 95.31% in related work. Moreover, it outperformed traditional optical character recognition by 28.25% and deep-learning EasyOCR by 14.18%. Its potential applications in law enforcement, traffic management, and parking systems can improve surveillance and security through automation. , Thesis (MSc) -- Faculty of Science, Computer Science, 2024
- Full Text:
- Authors: Boby, Alden Zachary
- Date: 2024-10-11
- Subjects: Automobile theft South Africa , Deep learning (Machine learning) , Object detection , YOLOv7 , YOLO , Pattern recognition systems , Image processing Digital techniques , Automobile license plates
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/464322 , vital:76501
- Description: Vehicle security is a growing concern for citizens of South Africa. Law enforcement relies on reports and security camera footage for vehicle identification but struggles to match the increasing number of carjacking incidents and low vehicle recovery rates. Security camera footage offers an accessible means to identify stolen vehicles, yet it often poses hurdles like anamorphic plates and low resolution. Furthermore, depending on human operators proves inefficient, requiring faster processes to improve vehicle recovery rates and trust in law enforcement. The integration of deep learning has revolutionised object detection algorithms, increasing the popularity of vehicle tracking for security purposes. This thesis investigates advanced deep-learning methods for a comprehensive vehicle search and re-identification system. It enhances YOLOv7’s algorithmic capabilities and employs preprocessing techniques like super-resolution and perspective correction via the Improved Warped Planar Object Detection network for more effective licence plate optical character recognition. Key contributions include a specifically annotated dataset for training object detection models, an optical character recognition model based on YOLOv7, and a method for identifying vehicles in unrestricted data. The system detected rectangular and square licence plates without prior shape knowledge, achieving a 98.7% character recognition rate compared to 95.31% in related work. Moreover, it outperformed traditional optical character recognition by 28.25% and deep-learning EasyOCR by 14.18%. Its potential applications in law enforcement, traffic management, and parking systems can improve surveillance and security through automation. , Thesis (MSc) -- Faculty of Science, Computer Science, 2024
- Full Text:
Selected medicinal plants leaves identification: a computer vision approach
- Authors: Deyi, Avuya
- Date: 2023-10-13
- Subjects: Deep learning (Machine learning) , Machine learning , Convolutional neural network , Computer vision in medicine , Medicinal plants
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424552 , vital:72163
- Description: Identifying and classifying medicinal plants are valuable and essential skills during drug manufacturing because several active pharmaceutical ingredients (API) are sourced from medicinal plants. For many years, identifying and classifying medicinal plants have been exclusively done by experts in the domain, such as botanists, and herbarium curators. Recently, powerful computer vision technologies, using machine learning and deep convolutional neural networks, have been developed for classifying or identifying objects on images. A convolutional neural network is a deep learning architecture that outperforms previous advanced approaches in image classification and object detection based on its efficient features extraction on images. In this thesis, we investigate different convolutional neural networks and machine learning algorithms for identifying and classifying leaves of three species of the genus Brachylaena. The three species considered are Brachylaena discolor, Brachylaena ilicifolia and Brachylaena elliptica. All three species are used medicinally by people in South Africa to treat diseases like diabetes. From 1259 labelled images of those plants species (at least 400 for each species) split into training, evaluation and test sets, we trained and evaluated different deep convolutional neural networks and machine learning models. The VGG model achieved the best results with 98.26% accuracy from cross-validation. , Thesis (MSc) -- Faculty of Science, Mathematics, 2023
- Full Text:
- Authors: Deyi, Avuya
- Date: 2023-10-13
- Subjects: Deep learning (Machine learning) , Machine learning , Convolutional neural network , Computer vision in medicine , Medicinal plants
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424552 , vital:72163
- Description: Identifying and classifying medicinal plants are valuable and essential skills during drug manufacturing because several active pharmaceutical ingredients (API) are sourced from medicinal plants. For many years, identifying and classifying medicinal plants have been exclusively done by experts in the domain, such as botanists, and herbarium curators. Recently, powerful computer vision technologies, using machine learning and deep convolutional neural networks, have been developed for classifying or identifying objects on images. A convolutional neural network is a deep learning architecture that outperforms previous advanced approaches in image classification and object detection based on its efficient features extraction on images. In this thesis, we investigate different convolutional neural networks and machine learning algorithms for identifying and classifying leaves of three species of the genus Brachylaena. The three species considered are Brachylaena discolor, Brachylaena ilicifolia and Brachylaena elliptica. All three species are used medicinally by people in South Africa to treat diseases like diabetes. From 1259 labelled images of those plants species (at least 400 for each species) split into training, evaluation and test sets, we trained and evaluated different deep convolutional neural networks and machine learning models. The VGG model achieved the best results with 98.26% accuracy from cross-validation. , Thesis (MSc) -- Faculty of Science, Mathematics, 2023
- Full Text:
Semantic segmentation of astronomical radio images: a computer vision approach
- Authors: Kupa, Ramadimetse Sydil
- Date: 2023-03-29
- Subjects: Semantic segmentation , Radio astronomy , Radio telescopes , Deep learning (Machine learning) , Image segmentation
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/422378 , vital:71937
- Description: The new generation of radio telescopes, such as the MeerKAT, ASKAP (Australian Square Kilometre Array Pathfinder) and the future Square Kilometre Array (SKA), are expected to produce vast amounts of data and images in the petabyte region. Therefore, the amount of incoming data at a specific point in time will overwhelm any current traditional data analysis method being deployed. Deep learning architectures have been applied in many fields, such as, in computer vision, machine vision, natural language processing, social network filtering, speech recognition, machine translation, bioinformatics, medical image analysis, and board game programs. They have produced results which are comparable to human expert performance. Hence, it is appealing to apply it to radio astronomy data. Image segmentation is one such area where deep learning techniques are prominent. The images from the new generation of telescopes have a high density of radio sources, making it difficult to classify the sources in the image. Identifying and segmenting sources from radio images is a pre-processing step that occurs before sources are put into different classes. There is thus a need for automatic segmentation of the sources from the images before they can be classified. This work uses the Unet architecture (originally developed for biomedical image segmentation) to segment radio sources from radio astronomical images with 99.8% accuracy. After segmenting the sources we use OpenCV tools to detect the sources on the mask images, then the detection is translated to the original image where borders are drawn around each detected source. This process automates and simplifies the pre-processing of images for classification tools and any other post-processing tool that requires a specific source as an input. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2023
- Full Text:
- Authors: Kupa, Ramadimetse Sydil
- Date: 2023-03-29
- Subjects: Semantic segmentation , Radio astronomy , Radio telescopes , Deep learning (Machine learning) , Image segmentation
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/422378 , vital:71937
- Description: The new generation of radio telescopes, such as the MeerKAT, ASKAP (Australian Square Kilometre Array Pathfinder) and the future Square Kilometre Array (SKA), are expected to produce vast amounts of data and images in the petabyte region. Therefore, the amount of incoming data at a specific point in time will overwhelm any current traditional data analysis method being deployed. Deep learning architectures have been applied in many fields, such as, in computer vision, machine vision, natural language processing, social network filtering, speech recognition, machine translation, bioinformatics, medical image analysis, and board game programs. They have produced results which are comparable to human expert performance. Hence, it is appealing to apply it to radio astronomy data. Image segmentation is one such area where deep learning techniques are prominent. The images from the new generation of telescopes have a high density of radio sources, making it difficult to classify the sources in the image. Identifying and segmenting sources from radio images is a pre-processing step that occurs before sources are put into different classes. There is thus a need for automatic segmentation of the sources from the images before they can be classified. This work uses the Unet architecture (originally developed for biomedical image segmentation) to segment radio sources from radio astronomical images with 99.8% accuracy. After segmenting the sources we use OpenCV tools to detect the sources on the mask images, then the detection is translated to the original image where borders are drawn around each detected source. This process automates and simplifies the pre-processing of images for classification tools and any other post-processing tool that requires a specific source as an input. , Thesis (MSc) -- Faculty of Science, Physics and Electronics, 2023
- Full Text:
- «
- ‹
- 1
- ›
- »