Hazry Desa
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Hazry Desa
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Desa, Hazry
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Desa, H.
Hazry, Desa
Desa, Hazy
Hazry, D.
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16642497100

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Real-time recognition and decision making of objects using deep learning ENet based UAV images

2023-03-29 , Ahmed S.A. , Hazry Desa , Hussain A.S.T.

Unmanned Aerial Vehicles (UAVs) have been found to have many uses in the maintenance and oversight of civil infrastructure assets. They contribute to scheduled bridge checkups, crisis control, electricity transmission cable oversight and traffic analysis. With more and more uses of UAVs being introduced, a greater focus on individuality and freedom regarding governance of these devices is required to ensure security, competency, and precision. The subject of this study outlines the method and policies to be followed for teaching the principals of the (efficient Neural Network) ENet architecture, machine learning, and using OpenCV to implement semantic segmentation on a collection of images obtained through aerial photography for identification of objects. Possible utilizations of UAVs in the area of transportation are mentioned as well along with the precision and efficiency of training for the application of the ENet architecture, machine learning, and OpenCV to implement semantic segmentation, the optimization selection of operational parameters, and the machine learning and ENet architecture teaching methods and policies drafting process. Through analysis of the object identification results, it was proven that by adhering to a specific set of parameters, the ENet architecture and machine learning procedures can successfully identify objects with an accuracy of 99% when there is no distortion. In addition, using a combination of all three technologies mentioned, it is possible to not only classify objects, but the device is also capable of automated tracking and detection of the objects by real-time processing of streamed videos by the UAVs. The novelty, that the ENET was applied for large class members difference distance among the same objects family.

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Automated RFID-Based Attendance and Access Control System for Efficient Workforce Management

2023-01-01 , Hussain A.S.T. , Taha T.A. , Ahmed S.R. , Ahmed S.A. , Ahmed O.K. , Hazry Desa

This paper focuses on designing an automated attendance and access control system using Radio Frequency Identification (RFID) technology. The current methods used by companies to track employee attendance are often inefficient and prone to errors. The proposed system aims to improve the process by automatically recording the working hours of employees using RFID tags in the form of ID cards. The RFID system consists of three components: an antenna, a transceiver, and transponders (tags). The antenna transmits a signal to activate the tag, which then transmits data back to the antenna. Unlike barcodes, RFID tags can be read from a distance and through various materials. The ID number on the RFID tag corresponds to the user's information stored in a database. The work's objective is to efficiently manage and analyze attendance data according to workplace regulations. The implementation includes designing the system using a PIC16F876A microcontroller, simulating the system with Proteus software, and analyzing the performance of the hardware and software components. The paper's scope encompasses its applicability in both educational institutions and industries, ensuring effective modeling and evaluation of the RFID-based attendance system.

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Classification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application

2023-06-01 , Ahmed S.A. , Hazry Desa , Hussain A.S.T.

The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.

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GPS and GSM Based Vehicle Tracking System

2023-01-01 , Hussain A.S.T. , Fadhil M. , Taha T.A. , Ahmed O.K. , Ahmed S.A. , Hazry Desa

Vehicle theft is a significant problem that affects various types of vehicle owners, ranging from those with motorcycles to those with cars, within the context of today's progressive societies. Nevertheless, implementing lost vehicle tracking systems can be financially burdensome, thus presenting challenges for individuals with limited income to access such solutions. Moreover, in situations where vehicles are stolen, owners often rely on authorities' ability to track signals, leaving them dependent on official intervention to recover their stolen vehicles. To address these challenges, our study has taken the initiative to develop a comprehensive vehicle tracking system that empowers owners to track their own vehicles autonomously. Based on the adaptable PIC microcontroller, this cutting-edge system integrates GPS and GSM technologies as well as a practical and functional implementation circuit. By providing an affordable and effective solution, our project strives to enhance vehicle security, minimize dependence on external assistance, and offer peace of mind to vehicle owners in navigating the complexities of vehicle theft and recovery.

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Effect of Spraying Dispersion Using UAV Spraying System with Different Height at Paddy Field

2023-01-01 , Hazry Desa , Muhammad Azizi Azizan , Nik Noriman Zulkepli , Nurfadzillah Ishak , Hang T.X. , Yahya S.S. , Shahrazel A.A.M. , Mansor F.M. , Aziz S.Z.A. , Hussain A.S.T.

This study investigated the UAV spraying system height in relation to spraying uniformity and dispersion. The operating heights of the UAV spraying system at heights of 1 m, 1.5 m, and 2 m from the hollow cone nozzles were investigated within a wind speed of 2.8 m/s. The tests were to determine the spray uniformity and dispersion on the water sensitive paper that was placed on the paddy plant. The results of water droplet samples were evaluated using ImageJ software. The results show the droplet distribution at 1.5 m height has high values for average droplet density, which is 162.7 deposits/cm²at the top area and 161.8 deposits/cm²at the bottom area. The percentage of coverage was also high, at 55.21% at the top area and 51.4% at the bottom area.

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Advancements in UAV image semantic segmentation: A comprehensive literature review

2024-06-01 , Ahmed S.A. , Hazry Desa , Easa H.K. , Hussain A.S.T. , Taha T.A. , Salih S.Q. , Hasan R.A. , Ahmed O.K. , Ng P.S.J.

Unmanned Aerial Vehicles (UAVs) have revolutionized data acquisition across various domains, presenting immense potential for image processing and semantic segmentation. This literature review encompasses a thorough exploration of advancements, techniques, challenges, and datasets pertaining to UAV image semantic segmentation. It begins by introducing the fundamental concepts of UAVs, highlighting their pivotal role in capturing high-resolution imagery that serves diverse applications. The integration of deep learning algorithms with UAVs is emphasized, unlocking new horizons in autonomous flight, security, and environmental monitoring. Delving into the core principles of semantic segmentation, the review elucidates the critical task of classifying every pixel in an image. Convolutional Neural Networks (CNNs) are presented as the cornerstone technology, tracing their evolution from traditional CNNs to the highly adaptable Fully Convolutional Networks (FCNs). A substantial portion of the review is dedicated to FCNs, underscoring their ability to process images of varying dimensions while maintaining spatial coherence in the output. Their pivotal role in semantic segmentation, encompassing both classification and localization, is articulated. The subsequent sections delve into a comprehensive survey of state-of-the-art models, including SegNet, PSPNet, DeepLabNet, EfficientNet, DenseNet-C, and LinkNet. Each model's unique strengths and applications contribute to the evolving landscape of semantic segmentation tasks. The versatility of the U-Net architecture takes center stage in the latter parts of the review. Its fundamental structure is elucidated, followed by a comprehensive examination of its manifold adaptations—3D-U-Net, ResU-Net, U-Net++, Adversarial U-Net, Cascaded U-Net, and Improved U-Net 3+. These modifications address intrinsic challenges such as limited receptive fields and class imbalances, propelling U-Net to the forefront of image segmentation techniques. The subsequent sections pivot toward the application of U-Net in UAV image segmentation, illustrating its efficacy in diverse tasks, including land cover and crop classification. Nevertheless, persisting challenges, such as the scarcity of annotated datasets and the need for model generalization across varied environmental conditions, remain key areas of concern. The review culminates by underlining the significance of large, authentic datasets and data augmentation techniques. Furthermore, a brief exploration of publicly available UAV image datasets is presented, enhancing our understanding of the resources accessible for training and evaluating models. This comprehensive literature review encapsulates the dynamism of UAV image processing and semantic segmentation, illuminating recent developments and avenues for future research in this burgeoning field.

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Aerial image semantic segmentation based on 3D fits a small dataset of 1D

2023-12-01 , Ahmed S.A. , Hazry Desa , Hussain A.S.T.

Time restrictions and lack of precision demand that the initial technique be abandoned. Even though the remaining datasets had fewer identified classes than initially planned for the study, the labels were more accurate. Because of the need for additional data, a single network cannot categorize all the essential elements in a picture, including bodies of water, roads, trees, buildings, and crops. However, the final network gains some invariance in detecting these classes with environmental changes due to the different geographic positions of roads and buildings discovered in the final datasets, which could be valuable in future navigation research. At the moment, binary classifications of a single class are the only datasets that can be used for the semantic segmentation of aerial images. Even though some pictures have more than one classification, images of roads and buildings were only found in a significant number of samples. Then, the building datasets were pooled to produce a larger dataset and for the constructed models to gain some invariance on image location. Because of the massive disparity in sample size, road datasets needed to be integrated.

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Definite time over-current protection on transmission line using MATLAB/Simulink

2024-04-01 , Taha T.A. , Zaynal H.I. , Hussain A.S.T. , Hazry Desa , Taha F.H.

This paper has investigated the application of the definite time over-current (DTOC) which reacts to protect the breaker from damage during the occurrence of over-current in the transmission lines. After a distance relay, this kind of over-current relay is utilized as backup protection. The over-current relay will provide a signal after a predetermined amount of time delay, and the breaker will trip if the distance relay does not detect a line failure. As a result, this over-current relay functions with a time delay that is just slightly longer than the combined working times of the distance relay and the breaker. This DTOC is tested for various types of faults which are 3-phase fault occurring at load 1, 3-phase fault occurring at load 2, a 3-phase fault occurring before primary protection, and the behaviour of voltage and current with a failed primary protection. All the results will be obtained using the MATLAB/Simulink software package.

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Development of IoT-Enabled Smart Water Metering System

2024-01-01 , Wen S.D. , Hazry Desa , Muhammad Azizi Azizan , Hussain A.S.T. , Tanveer M.H. , Patan R.

This paper introduces a smart water meter that utilizes the capabilities of the Internet of Things (IoT) to automate the collection of meter readings. The primary goal of this project is to create an IoT-based device for reading water meters, while simultaneously developing a compatible mobile application. Instead of relying on manual meter reading, which requires human effort, this project proposes the use of an IoT-enabled water meter to collect the data automatically. The device employs a camera and Convolutional Neural Network (CNN) for image processing, making it easy to detect the meter reading accurately. The IoT system architecture involves the use of an ESP32 CAM for data collection, a laptop as a gateway, and the Message Queuing Telemetry Transport (MQTT) protocol for data transfer. The collected data is stored in Firebase's real-time database, and the mobile application is designed to monitor and analyze the data. A functional prototype of the device is constructed and tested in a housing area. The collected data is then monitored through the developed mobile application. Lastly, the data is analyzed to assess the suitability of the proposed method, and recommendations for future improvements are provided.

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Implementation of deep neural networks learning on unmanned aerial vehicle based remote-sensing

2024-03-01 , Ahmed S.A. , Hazry Desa , Hussain A.S.T. , Taha T.A.

Due to efficient and adaptable data collecting, unmanned aerial vehicle (UAV) has been a popular topic in computer vision (CV) and remote sensing (RS) in recent years. Inspiring by the recent success of deep learning (DL), several enhanced object identification and tracking methods have been broadly applied to a variety of UAV-related applications, including environmental monitoring, precision agriculture, and traffic management. In this research, we present efficient neural network (ENet), a unique deep neural network architecture designed exclusively for jobs demanding low latency operation. ENet is up to quicker, takes fewer floating-point operations per second (FLOPs), has fewer parameters, and offers accuracy comparable to or superior to that of previous models. We have tested it on the street and cityscapes reports on comparisons with current state-of-the-art approaches and the tradeoffs between a network's processing speed and accuracy. We give measurements of the proposed architecture's performance on embedded devices and offer software enhancements that might make ENet even quicker.

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