Enabling Vehicle Search Through Robust Licence Plate Detection
- 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.
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- Date Issued: 2023
Plant Disease Detection using Vision Transformers on Multispectral Natural Environment Images
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463456 , vital:76410 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220517"
- Description: Enhancing agricultural practices has become essential in mitigating global hunger. Over the years, significant technological advancements have been introduced to improve the quality and quantity of harvests by effectively managing weeds, pests, and diseases. Many studies have focused on identifying plant diseases, as this information aids in making informed decisions about applying fungicides and fertilizers. Advanced systems often employ a combination of image processing and deep learning techniques to identify diseases based on visible symptoms. However, these systems typically rely on pre-existing datasets or images captured in controlled environments. This study showcases the efficacy of utilizing multispectral images captured in visible and Near Infrared (NIR) ranges for identifying plant diseases in real-world environmental conditions. The collected datasets were classified using popular Vision Transformer (ViT) models, including ViT- S16, ViT-BI6, ViT-LI6 and ViT-B32. The results showed impressive training and test accuracies for all the data collected using diverse Kolari vision lenses with 93.71 % and 90.02 %, respectively. This work highlights the potential of utilizing advanced imaging techniques for accurate and reliable plant disease identification in practical field conditions.
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- Date Issued: 2023
Real-Time Detecting and Tracking of Squids Using YOLOv5
- 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.
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- Date Issued: 2023
Spatiotemporal Convolutions and Video Vision Transformers for Signer-Independent Sign Language Recognition
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2023
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/463478 , vital:76412 , xlink:href="https://ieeexplore.ieee.org/abstract/document/10220534"
- Description: Sign language is a vital tool of communication for individuals who are deaf or hard of hearing. Sign language recognition (SLR) technology can assist in bridging the communication gap between deaf and hearing individuals. However, existing SLR systems are typically signer-dependent, requiring training data from the specific signer for accurate recognition. This presents a significant challenge for practical use, as collecting data from every possible signer is not feasible. This research focuses on developing a signer-independent isolated SLR system to address this challenge. The system implements two model variants on the signer-independent datasets: an R(2+ I)D spatiotemporal convolutional block and a Video Vision transformer. These models learn to extract features from raw sign language videos from the LSA64 dataset and classify signs without needing handcrafted features, explicit segmentation or pose estimation. Overall, the R(2+1)D model architecture significantly outperformed the ViViT architecture for signer-independent SLR on the LSA64 dataset. The R(2+1)D model achieved a near-perfect accuracy of 99.53% on the unseen test set, with the ViViT model yielding an accuracy of 72.19 %. Proving that spatiotemporal convolutions are effective at signer-independent SLR.
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- Date Issued: 2023
An evaluation of hand-based algorithms for sign language recognition
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465124 , vital:76575 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856310"
- Description: Sign language recognition is an evolving research field in computer vision, assisting communication between hearing disabled people. Hand gestures contain the majority of the information when signing. Focusing on feature extraction methods to obtain the information stored in hand data in sign language recognition may improve classification accuracy. Pose estimation is a popular method for extracting body and hand landmarks. We implement and compare different feature extraction and segmentation algorithms, focusing on the hands only on the LSA64 dataset. To extract hand landmark coordinates, MediaPipe Holistic is implemented on the sign images. Classification is performed using poplar CNN architectures, namely ResNet and a Pruned VGG network. A separate 1D-CNN is utilised to classify hand landmark coordinates extracted using MediaPipe. The best performance was achieved on the unprocessed raw images using a Pruned VGG network with an accuracy of 95.50%. However, the more computationally efficient model using the hand landmark data and 1D-CNN for classification achieved an accuracy of 94.91%.
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- Date Issued: 2022
Investigating the Effects of Image Correction Through Affine Transformations on Licence Plate Recognition
- 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/10962/465190 , vital:76581 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9856380"
- Description: Licence plate recognition has many real-world applications, which fall under security and surveillance. Deep learning for licence plate recognition has been adopted to improve existing image-based processing techniques in recent years. Object detectors are a popular choice for approaching this task. All object detectors are some form of a convolutional neural network. The You Only Look Once framework and Region-Based Convolutional Neural Networks are popular models within this field. A novel architecture called the Warped Planar Object Detector is a recent development by Zou et al. that takes inspiration from YOLO and Spatial Network Transformers. This paper aims to compare the performance of the Warped Planar Object Detector and YOLO on licence plate recognition by training both models with the same data and then directing their output to an Enhanced Super-Resolution Generative Adversarial Network to upscale the output image, then lastly using an Optical Character Recognition engine to classify characters detected from the images.
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- Date Issued: 2022
Plant disease detection using deep learning on natural environment images
- Authors: De Silva, Malitha , Brown, Dane L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465212 , vital:76583 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9855925"
- Description: Improving agriculture is one of the major concerns today, as it helps reduce global hunger. In past years, many technological advancements have been introduced to enhance harvest quality and quantity by controlling and preventing weeds, pests, and diseases. Several studies have focused on identifying diseases in plants, as it helps to make decisions on spraying fungicides and fertilizers. State-of-the-art systems typically combine image processing and deep learning methods to identify conditions with visible symptoms. However, they use already available data sets or images taken in controlled environments. This study was conducted on two data sets of ten plants collected in a natural environment. The first dataset contained RGB Visible images, while the second contained Near-Infrared (NIR) images of healthy and diseased leaves. The visible image dataset showed higher training and validation accuracies than the NIR image dataset with ResNet, Inception, VGG and MobileNet architectures. For the visible image and NIR dataset, ResNet-50V2 outperformed other models with validation accuracies of 98.35% and 94.01%, respectively.
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- Date Issued: 2022
An Evaluation of YOLO-Based Algorithms for Hand Detection in the Kitchen
- Authors: Van Staden, Joshua , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465134 , vital:76576 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519307"
- Description: Convolutional Neural Networks have offered an accurate method with which to run object detection on images. Specifically, the YOLO family of object detection algorithms have proven to be relatively fast and accurate. Since its inception, the different variants of this algorithm have been tested on different datasets. In this paper, we evaluate the performances of these algorithms on the recent Epic Kitchens-100 dataset. This dataset provides egocentric footage of people interacting with various objects in the kitchen. Most prominently shown in the footage is an egocentric view of the participants' hands. We aim to use the YOLOv3 algorithm to detect these hands within the footage provided in this dataset. In particular, we examine the YOLOv3 algorithm using two different backbones: MobileNet-lite and VGG16. We trained them on a mixture of samples from the Egohands and Epic Kitchens-100 datasets. In a separate experiment, average precision was measured on an unseen Epic Kitchens-100 subset. We found that the models are relatively simple and lead to lower scores on the Epic Kitchens 100 dataset. This is attributed to the high background noise on the Epic Kitchens 100 dataset. Nonetheless, the VGG16 architecture was found to have a higher Average Precision (AP) and is, therefore, more suited for retrospective analysis. None of the models was suitable for real-time analysis due to complex egocentric data.
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- Date Issued: 2021
Handwriting Recognition using Deep Learning with Effective Data Augmentation Techniques
- Authors: Brown, Dane L , Lidzhade, Ipfi
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465700 , vital:76633 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519359"
- Description: Machine learning techniques have been successfully used in deciphering handwritten text. Deep learning has made further improvements in this regard. However, they require substantial amounts of training data. This research aims to improve the effectiveness of classification accuracy in the presence of limited training data on handwriting recognition. The main focus thus involves enabling deep models to converge during training on smaller datasets using data augmentation. This will allow for broader use of these systems across more regions, greater accessibility, and future related systems to be less reliant on the amount of data available. Therefore, the proposed research includes an image processing and machine learning approach to handwriting recognition while generating more sample data in various ways. Applying random cropping as an augmentation technique resulted in higher accuracy than several other augmentation techniques examined in this paper. Some of these techniques performed worse than on unaugmented data.
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- Date Issued: 2021
Investigating popular CNN architectures for plant disease detection
- Authors: Poole, Louise C , Brown, Dane L
- Date: 2021
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465168 , vital:76579 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9519341"
- Description: Food production and food security have become increasingly important due to climate change and rising population numbers. One method to prevent crop loss is to develop a system to allow for early, efficient and accurate identification of plant diseases. CNNs often outperform previously popular machine learning algorithms. There are many existing CNN architectures. We compared and analysed the popular state-of-the-art architectures, namely ResNet, GoogLeNet and VGG, when trained for plant disease classification. We found that ResNet performed the best on the balanced Mendeley Leaves and PlantVillage datasets, obtaining 91.95% and 95.80% accuracy respectively. However, the ResNet architecture was relatively computationally expensive and slow to train. GoogLeNet obtained accuracies very close to those of ResNet with 89.35% and 94.59% achieved on the Mendeley Leaves and PlantVillage datasets respectively and could be considered a less computationally expensive alternative.
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- Date Issued: 2021
Multi-angled face segmentation and identification using limited data
- Authors: Brown, Dane L
- Date: 2019
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465711 , vital:76634 , xlink:href="https://ieeexplore.ieee.org/abstract/document/9067899"
- Description: This paper introduces a different approach to face segmentation that aims to improve face recognition when given large pose angles and limited training data. Face segmentation is achieved by extracting landmarks which are manipulated in such a way as to normalize unseen data with a classification model. The approach is compared with related systems, followed by further tests that show consistent results across other datasets. Experiments include frontal and non-frontal training images for classification of various face pose angles. The proposed system is a promising contribution, and especially shows the importance of face segmentation. The results are achieved using minimal training data, such that both accurate and practical face recognition systems can be constructed.
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- Date Issued: 2019
Comparison of fluorophore and peroxidase labeled aptamer assays for MUC1 detection in cancer cells
- Authors: Flanagan, Shane , Limson, Janice , Fogel, Ronen
- Date: 2014
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/431076 , vital:72742 , xlink:href="10.1109/BioCAS.2014.6981720"
- Description: Aptamers hold great promise for cancer diagnosis and therapy. Several biosensors incorporate aptamers as biorecognition elements for tumor markers although few evaluate their detection in a native conformation and cellular micro-environment. In this study, fluorophore and peroxidase labeled aptamer configurations were compared for the detection of MCF7 breast and SW620 colon cancer cell lines expressing the tumor marker MUC1. Fluorescence based detection showed selective binding to the cell lines relative to a nonbinding control sequence with sequence specific binding differences between MUC1 aptamers accredited to variation in the glycosylation state of expressed MUC1. The peroxidase labeled assay showed high detection sensitivity although low binding specificity was observed for the MUC1 aptamers to the cell lines. Results suggest that aptamers susceptible to non specific binding to cells may limit the applicability of enzymatic amplification to improve aptasensor sensitivity.
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- Date Issued: 2014
Electrochemical inclusion of catechol into singlewalled carbon nanotubes: application for sensors
- Authors: Oni, Joshua , Limson, Janice
- Date: 2014
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/431090 , vital:72743 , xlink:href="10.1109/BioCAS.2014.6981727"
- Description: We report on the use of catechol for the electrochemical activation of acid-functionalised single-walled carbon nanotubes immobilised on glassy carbon electrodes. Following well-published methods for catechol activation of bare glassy carbon electrodes, these studies show the efficacy of extending the method to activation of carbon nanotubes. Voltammetric scans in catechol show an increase in current response of 37 μA for the catechol redox pair over a maximum of three cycles during the catechol activation step. An increase in the ease of electron flow is indicated by a larger value for K app , which corresponds to a decrease in R ct obtained during impedance measurements. Catechol activation enhanced electron transfer, potentially afforded by an ease of electron passage due to a decrease in the resistance of the layer.
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- Date Issued: 2014