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
Plant disease detection using deep learning on natural environment images
- 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/10962/465212 , 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.
- 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/10962/465212 , 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.
- Full Text:
- Date Issued: 2022
Investigating popular CNN architectures for plant disease detection
- Poole, Louise C, Brown, Dane L
- 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.
- Full Text:
- Date Issued: 2021
- 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.
- Full Text:
- Date Issued: 2021
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