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25 pages, 1890 KiB

Open AccessArticle

Integrating Few-Shot Learning and Multimodal Image Enhancement in GNut: A Novel Approach to Groundnut Leaf Disease Detection

by Imran Qureshi

Computers 2024, 13(12), 306; https://doi.org/10.3390/computers13120306 - 22 Nov 2024

Abstract

Groundnut is a vital crop worldwide, but its production is significantly threatened by various leaf diseases. Early identification of such diseases is vital for maintaining agricultural productivity. Deep learning techniques have been employed to address this challenge and enhance the detection, recognition, and [...] Read more.

Groundnut is a vital crop worldwide, but its production is significantly threatened by various leaf diseases. Early identification of such diseases is vital for maintaining agricultural productivity. Deep learning techniques have been employed to address this challenge and enhance the detection, recognition, and classification of groundnut leaf diseases, ensuring better management and protection of this important crop. This paper presents a new approach to the detection and classification of groundnut leaf diseases by the use of an advanced deep learning model, GNut, which integrates ResNet50 and DenseNet121 architectures for feature extraction and Few-Shot Learning (FSL) for classification. The proposed model overcomes groundnut crop diseases by addressing an efficient and highly accurate method of managing diseases in agriculture. Evaluated on a novel Pak-Nuts dataset collected from groundnut fields in Pakistan, the GNut model achieves promising accuracy rates of 99% with FSL and 95% without it. Advanced image preprocessing techniques, such as Multi-Scale Retinex with Color Restoration and Adaptive Histogram Equalization and Multimodal Image Enhancement for Vegetative Feature Isolation were employed to enhance the quality of input data, further improving classification accuracy. These results illustrate the robustness of the proposed model in real agricultural applications, establishing a new benchmark for groundnut leaf disease detection and highlighting the potential of AI-powered solutions to play a role in encouraging sustainable agricultural practices. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

20 pages, 4520 KiB

Open AccessArticle

Employing Different Algorithms of Lightweight Convolutional Neural Network Models in Image Distortion Classification

by Ismail Taha Ahmed, Falah Amer Abdulazeez and Baraa Tareq Hammad

Computers 2024, 13(10), 268; https://doi.org/10.3390/computers13100268 - 12 Oct 2024

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Abstract

The majority of applications use automatic image recognition technologies to carry out a range of tasks. Therefore, it is crucial to identify and classify image distortions to improve image quality. Despite efforts in this area, there are still many challenges in accurately and [...] Read more.

The majority of applications use automatic image recognition technologies to carry out a range of tasks. Therefore, it is crucial to identify and classify image distortions to improve image quality. Despite efforts in this area, there are still many challenges in accurately and reliably classifying distorted images. In this paper, we offer a comprehensive analysis of models of both non-lightweight and lightweight deep convolutional neural networks (CNNs) for the classification of distorted images. Subsequently, an effective method is proposed to enhance the overall performance of distortion image classification. This method involves selecting features from the pretrained models’ capabilities and using a strong classifier. The experiments utilized the kadid10k dataset to assess the effectiveness of the results. The K-nearest neighbor (KNN) classifier showed better performance than the naïve classifier in terms of accuracy, precision, error rate, recall and F1 score. Additionally, SqueezeNet outperformed other deep CNN models, both lightweight and non-lightweight, across every evaluation metric. The experimental results demonstrate that combining SqueezeNet with KNN can effectively and accurately classify distorted images into the correct categories. The proposed SqueezeNet-KNN method achieved an accuracy rate of 89%. As detailed in the results section, the proposed method outperforms state-of-the-art methods in accuracy, precision, error, recall, and F1 score measures. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

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18 pages, 8530 KiB

Open AccessArticle

Spatiotemporal Bayesian Machine Learning for Estimation of an Empirical Lower Bound for Probability of Detection with Applications to Stationary Wildlife Photography

by Mohamed Jaber, Robert D. Breininger, Farag Hamad and Nezamoddin N. Kachouie

Computers 2024, 13(10), 255; https://doi.org/10.3390/computers13100255 - 8 Oct 2024

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Abstract

An important parameter in the monitoring and surveillance systems is the probability of detection. Advanced wildlife monitoring systems rely on camera traps for stationary wildlife photography and have been broadly used for estimation of population size and density. Camera encounters are collected for [...] Read more.

An important parameter in the monitoring and surveillance systems is the probability of detection. Advanced wildlife monitoring systems rely on camera traps for stationary wildlife photography and have been broadly used for estimation of population size and density. Camera encounters are collected for estimation and management of a growing population size using spatial capture models. The accuracy of the estimated population size relies on the detection probability of the individual animals, and in turn depends on observed frequency of the animal encounters with the camera traps. Therefore, optimal coverage by the camera grid is essential for reliable estimation of the population size and density. The goal of this research is implementing a spatiotemporal Bayesian machine learning model to estimate a lower bound for probability of detection of a monitoring system. To obtain an accurate estimate of population size in this study, an empirical lower bound for probability of detection is realized considering the sensitivity of the model to the augmented sample size. The monitoring system must attain a probability of detection greater than the established empirical lower bound to achieve a pertinent estimation accuracy. It was found that for stationary wildlife photography, a camera grid with a detection probability of at least 0.3 is required for accurate estimation of the population size. A notable outcome is that a moderate probability of detection or better is required to obtain a reliable estimate of the population size using spatiotemporal machine learning. As a result, the required probability of detection is recommended when designing an automated monitoring system. The number and location of cameras in the camera grid will determine the camera coverage. Consequently, camera coverage and the individual home-range verify the probability of detection. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

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17 pages, 3728 KiB

Open AccessArticle

YOLOv8-Based Drone Detection: Performance Analysis and Optimization

by Betul Yilmaz and Ugurhan Kutbay

Computers 2024, 13(9), 234; https://doi.org/10.3390/computers13090234 - 17 Sep 2024

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Abstract

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of drones. The purpose of this study [...] Read more.

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of drones. The purpose of this study is the evaluation of deep learning approaches such as pre-trained YOLOv8 drone detection for security issues. This study focuses on the YOLOv8 model to achieve optimal performance in object detection tasks using a publicly available dataset collected by Mehdi Özel for a UAV competition that is sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. YOLOv8, known for its advanced architecture, was selected due to its suitability for real-time detection applications and its ability to process complex visual data. Hyperparameter tuning and data augmentation techniques were applied to maximize the performance of the model. Basic hyperparameters such as learning rate, batch size, and optimization settings were optimized through iterative experiments to provide the best performance. In addition to hyperparameter tuning, various data augmentation strategies were used to increase the robustness and generalization ability of the model. Techniques such as rotation, scaling, flipping, and color adjustments were applied to the dataset to simulate different conditions and variations. Among the augmentation techniques applied to the specific dataset in this study, rotation was found to deliver the highest performance. Blurring and cropping methods were observed to follow closely behind. The combination of optimized hyperparameters and strategic data augmentation allowed YOLOv8 to achieve high detection accuracy and reliable performance on the publicly available dataset. This method demonstrates the effectiveness of YOLOv8 in real-world scenarios, while also highlighting the importance of hyperparameter tuning and data augmentation in increasing model capabilities. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605, and a considerable precision–recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

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18 pages, 5905 KiB

Open AccessArticle

Detection of Bus Driver Mobile Phone Usage Using Kolmogorov-Arnold Networks

by János Hollósi, Áron Ballagi, Gábor Kovács, Szabolcs Fischer and Viktor Nagy

Computers 2024, 13(9), 218; https://doi.org/10.3390/computers13090218 - 3 Sep 2024

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Abstract

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific [...] Read more.

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific task, a unique dataset was constructed consisting of images of bus drivers in two scenarios: driving without phone interaction and driving while on a phone call. This dataset provides the basis for the current research. Different KAN-based networks were developed for custom action recognition tailored to the nuanced task of identifying drivers holding phones. The system’s performance was evaluated against convolutional neural network-based solutions, and differences in accuracy and robustness were observed. The aim was to propose an appropriate solution for professional Driver Monitoring Systems (DMS) in research and development and to investigate the efficiency of KAN solutions for this specific sub-task. The implications of this work extend beyond enforcement, providing a foundational technology for automating monitoring and improving safety protocols in the commercial and public transport sectors. In conclusion, this study demonstrates the efficacy of KAN network layers in neural network designs for driver monitoring applications. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

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20 pages, 17178 KiB

Open AccessArticle

Stego-STFAN: A Novel Neural Network for Video Steganography

by Guilherme Fay Vergara, Pedro Giacomelli, André Luiz Marques Serrano, Fábio Lúcio Lopes de Mendonça, Gabriel Arquelau Pimenta Rodrigues, Guilherme Dantas Bispo, Vinícius Pereira Gonçalves, Robson de Oliveira Albuquerque and Rafael Timóteo de Sousa Júnior

Computers 2024, 13(7), 180; https://doi.org/10.3390/computers13070180 - 19 Jul 2024

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Abstract

This article presents an innovative approach to video steganography called Stego-STFAN, as by using a cheap model process to use the temporal and spatial domains together, they end up presenting fine adjustments in each frame, the Stego-STFAN had a [...] Read more.

This article presents an innovative approach to video steganography called Stego-STFAN, as by using a cheap model process to use the temporal and spatial domains together, they end up presenting fine adjustments in each frame, the Stego-STFAN had a

P S N R_{c}

metric of 27.03 and

P S N R_{S}

of 23.09, which is close to the state-of-art. Steganography is the ability to hide a message so that third parties cannot perceive communication between them. Thus, one of the precautions in steganography is the size of the message you want to hide, as the security of the message is inversely proportional to its size. Inspired by this principle, video steganography appears to expand channels further and incorporate data into a message. To improve the construction of better stego-frames and recovered secrets, we propose a new architecture for video steganography derived from the Spatial-Temporal Adaptive Filter Network (STFAN) in conjunction with the Attention mechanism, which together generates filters and maps dynamic frames to increase the efficiency and effectiveness of frame processing, exploiting the redundancy present in the temporal dimension of the video, as well as fine details such as edges, fast-moving pixels and the context of secret and cover frames and by using the DWT method as another feature extraction level, having the same characteristics as when applied to an image file. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

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19 pages, 1015 KiB

Open AccessArticle

A Regularized Physics-Informed Neural Network to Support Data-Driven Nonlinear Constrained Optimization

by Diego Armando Perez-Rosero, Andrés Marino Álvarez-Meza and Cesar German Castellanos-Dominguez

Computers 2024, 13(7), 176; https://doi.org/10.3390/computers13070176 - 18 Jul 2024

Viewed by 1057

Abstract

Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, [...] Read more.

Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as nonconvex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framework as a new NOPT tool for both supervised and unsupervised data-driven scenarios. Our RPINN is threefold: By using custom activation functions and regularization penalties in an artificial neural network (ANN), RPINN can handle data variability and noisy inputs. Furthermore, it employs physics principles to construct the network architecture, computing the optimization variables based on network weights and learned features. In addition, it uses automatic differentiation training to make the system scalable and cut down on computation time through batch-based back-propagation. The test results for both supervised and unsupervised NOPT tasks show that our RPINN can provide solutions that are competitive compared to state-of-the-art solvers. In turn, the robustness of RPINN against noisy input measurements makes it particularly valuable in environments with fluctuating information. Specifically, we test a uniform mixture model and a gas-powered system as NOPT scenarios. Overall, with RPINN, its ANN-based foundation offers significant flexibility and scalability. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

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40 pages, 5965 KiB

Open AccessSystematic Review

A Systematic Review and Comparative Analysis Approach to Boom Gate Access Using Plate Number Recognition

by Asaju Christine Bukola, Pius Adewale Owolawi, Chuling Du and Etienne Van Wyk

Computers 2024, 13(11), 286; https://doi.org/10.3390/computers13110286 - 4 Nov 2024

Viewed by 468

Abstract

Security has been paramount to many organizations for many years, with access control being one of the critical measures to ensure security. Among various approaches to access control, vehicle plate number recognition has received wide attention. However, its application to boom gate access [...] Read more.

Security has been paramount to many organizations for many years, with access control being one of the critical measures to ensure security. Among various approaches to access control, vehicle plate number recognition has received wide attention. However, its application to boom gate access has not been adequately explored. This study proposes a method to access the boom gate by optimizing vehicle plate number recognition. Given the speed and accuracy of the YOLO (You Only Look Once) object detection algorithm, this study proposes using the YOLO deep learning algorithm for plate number detection to access a boom gate. To identify the gap and the most suitable YOLO variant, the study systematically surveyed the publication database to identify peer-reviewed articles published between 2020 and 2024 on plate number recognition using different YOLO versions. In addition, experiments are performed on four YOLO versions: YOLOv5, YOLOv7, YOLOv8, and YOLOv9, focusing on vehicle plate number recognition. The experiments, using an open-source dataset with 699 samples in total, reported accuracies of 81%, 82%, 83%, and 73% for YOLO V5, V7, V8, and V9, respectively. This comparative analysis aims to determine the most appropriate YOLO version for the task, optimizing both security and efficiency in boom gate access control systems. By optimizing the capabilities of advanced YOLO algorithms, the proposed method seeks to improve the reliability and effectiveness of access control through precise and rapid plate number recognition. The result of the analysis reveals that each YOLO version has distinct advantages depending on the application’s specific requirements. In complex detection conditions with changing lighting and shadows, it was revealed that YOLOv8 performed better in terms of reduced loss rates and increased precision and recall metrics. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

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