The model examines each frame and anchor free detection and localizes fire-related items using bounding boxes anchor free detection determine the existence of fire. Исследование переносимости детекторов между синтетическими и реальными данными 4. To ensure the correct coverage of the entire parking area, you can use лазер технологический со22 following camera field of view Laser diodo 980 formula:. Преобразование изображений и методов анализа между доменами данных для одного и того же объекта для обеспечения инвариантности работы алгоритмов представляет собой сложную научную проблему. Thank You! Метод детектирования SSD 1.
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Student Theses
In this paper, we present a detailed study of a real-time parking space detection system using neural networks. We address the problem of optimizing urban mobility through smart infrastructure, focusing on real-time object detection. The study emphasizes the importance of automating tasks requiring speed and accuracy, such as traffic and parking management. The purpose of the research is to develop an automated parking meter based on object detection algorithms, particularly the YOLO algorithm.
Relevance of the study lies in the advanced object detection algorithms like YOLO , crucial for applications such as smart cities and surveillance systems. Yessenov Scientific supervisor - Kenzhebayeva Zh. Aktau city, Kazakhstan. Asbstract: In this paper, we present a detailed study of a real-time parking space detection system using neural networks. Relevance of the study lies in the advanced object detection algorithms like YOLO, crucial for applications such as smart cities and surveillance systems. Аннотация: В этой статье мы представляем подробное исследование системы обнаружения парковочных мест в режиме реального времени с использованием нейронных сетей.
Мы рассматриваем проблему оптимизации городской мобильности с помощью интеллектуальной инфраструктуры, уделяя особое внимание обнаружению объектов в режиме реального времени. В исследовании подчеркивается важность автоматизации задач, требующих скорости и точности, таких как управление дорожным движением и парковкой. Целью исследования является разработка автоматизированного парковочного счетчика на основе алгоритмов обнаружения объектов, в частности алгоритма YOLO. Актуальность исследования заключается в использовании передовых алгоритмов обнаружения объектов, таких как YOLO, которые имеют решающее значение для таких приложений, как "умные города" и системы видеонаблюдения. Аннотация: Бул жумыста б1з нейрондыц желшерд1 цолдана отырып, нацты уацыт режимтде турац орындарын аныцтау ЖYйесiн егжей-тегжейл1 зерттеуд1 усынамыз.
Б1з нацты уацыт режимтде объектiлердi аныцтауга баса назар аудара отырып, ацылды инфрацурылым арцылы цалалыц утцырлыцты оцтайландыру мэселест шешем1з. Зерттеу цозгалыс пен автотурацты басцару сияцты жылдамдыц пен дэлдiктi цажет ететт тапсырмаларды автоматтандырудыц мацыздылыгын кврсетедi. Зерттеудщ мацсаты-объектiлердi аныцтау алгоритмдерiне, атап айтцанда YOLO алгоритмiне негiзделген автоматтандырылган турац есептегшт жасау. Зерттеудщ взектшт "ацылды цалалар" жэне бацылау ЖYйелерi сияцты цолданбалар Yшiн вте мацызды YOLO сияцты жетiлдiрiлген нысандарды аныцтау алгоритмдертде. R-CNN жэне YOLO сияцты dpmypni эдгстердг салыстыра отырып, бул зерттеу дэлд1кт1, жылдамдыцты жэне есептеу тшмдштн тецестiру туралы тустж бередi.
Object recognition is a key technology in the field of computer vision that allows machines to identify and classify objects in images or videos. The history of object recognition can be traced back to the first attempts at pattern recognition, when basic algorithms were used to identify simple shapes. However, these methods were complex and limited in the possibilities of processing various visual data. The emergence of machine learning and, more recently, Deep Learning has marked a significant turning point in this area. Neural networks based on the structure and functions of the human brain have proven to be particularly effective in learning and recognizing patterns from large amounts of data.
Neural networks, the basic concept of artificial intelligence, have revolutionized various areas of computer vision, especially object recognition. These networks are based on the structure of the human brain and consist of interconnected nodes or "neurons" that simulate how the human brain processes information. A neural network architecture usually includes an input level, several hidden levels, and an output level. Each level transforms inputs into a more abstract representation, allowing the network to explore complex patterns and relationships within the data.
However, it was not until the s and s that neural networks became popular, mainly due to advances in computing power and the development of a reverse propagation algorithm by Rumelhart, Hinton and Williams in Reverse propagation allowed neural networks to iteratively adjust their weights to reduce the error in their predictions, making them more effective in data-based learning[1]. An important milestone in the evolution of neural networks was the development of convolutional neural networks CNNS by Jan LeCun and his colleagues in the late s and early s. CNN consists of several types of layers, including convolutional layers that combine layers, and fully connected layers.
Convolutional layers apply filters to the input image to identify local patterns, combined layers reduce image size while retaining important characteristics, and fully linked layers combine these characteristics to obtain a final prediction. This architecture has become the basis of modern algorithms for recognizing objects[2]. The development of object recognition algorithms has been marked by continuous innovations due to the increasing complexity of visual data and the increasing demand for more accurate and efficient models.
The first attempts to recognize objects in the s and s were based on manual methods of identifying signs, when engineers developed algorithms for identifying simple shapes and patterns in images. However, these approaches were limited by the ability to process the diversity and complexity of visual data in the real world[3]. In the s, object recognition methods based on machine learning appeared, which made it possible to automatically extract symbols from data. The methods of support vectors SVM and k-near neighbors k-NN were one of the popular algorithms of that era. Although these methods represented a significant improvement in comparison to manual object extraction, they still required significant pretreatment and were not very suitable for processing complex graphical data.
A real breakthrough in object recognition came in the s with the advent. The AlexNet architecture, which includes many convolu levels, ReLU activation, and drop-down levels, has set a new standard for object recognition and demonstrated deep learning potential in this area. After the success of Alexnet, many object recognition algorithms have been developed, each of which expands the boundaries of what is possible in terms of speed, accuracy, and efficiency.
Each of these algorithms has new technologies to improve the efficiency of object recognition, which allows systems to be used in real-time applications[4]. With the development of object recognition technology, several algorithms have become leaders in this area, each of which has its own methodology and field of application. The following is a detailed description of some of these basic algorithms:. The R-CNN approach involves creating approximately 2, regional recommendations for each image, and then running CNN for each region to classify the object. The layer that combines the area of interest RoI produces fixed-sized object vectors for each region proposal, which are then classified. This approach significantly reduces calculation costs and increases speed while maintaining high accuracy.
RPN shares convolutional levels with the object detection network, which allows it to generate region-wide recommendations faster than previous methods. This discovery made Faster R-CNN one of the most accurate and effective object detection algorithms at the time. Instead, the SSD directly predicts boundaries and class estimates for multiple objects of different scales based on object maps created by CNN. The SSD architecture is completely convolutional, which allows fast detection while maintaining high accuracy.
This made the SSD ideal for real-time object detection tasks. YOLO divides the input image into a grid, each cell of which predicts the boundaries and probabilities of classes in a single pass through the network. This approach allows YOLO to process images very quickly, making it one of the fastest object detection algorithms, albeit with lower accuracy compared to regional methods such as the Faster R-CNN[5]. The authors formulate the problem of object detection as a regression problem, and not a classification problem, separating the limiting. YOLO is a popular algorithm for detecting real-time objects. YOLO once combined a multistage process and uses a single neural network to classify and predict the boundaries of defined objects.
Thus, it is significantly optimized to increase detection performance and can work much faster than two separate neural networks to identify and classify objects individually. This is accomplished by changing the purpose of the traditional image classifiers used for the regression task to determine the constraint framework for objects. In this article, we will consider only yolov1, the first of many iterations that this architecture has passed. Although there were many improvements in later iterations, the basic idea of the architecture remained the same.
Simply called YOLO, YOLOv1 can perform object detection at 45fps faster than real-time, making it a great choice for applications that need real-time detection. It looks at the entire image at once and only once — hence the name "you only look once"-allowing it to capture the context of defined objects. This halves the number of false positives compared to R-CNN, which deals with different parts of the picture individually.
In addition, YOLO can generalize the views of different objects, which allows it to be applied to different new environments[7]. The architecture of the YOLO model consists of three main components: head, neck and spine. The spine is part of a network consisting of convolutional layers designed to identify and process the main features of the image. Neck uses objects of convolution layers in a trunk with fully connected layers to make predictions about the probabilities and coordinates of the limiting rectangle. The head layer is the last output layer of the network, which can be replaced with other layers with the same input form to teach transportation.
These three parts of the model work together first extract the main visual elements from the picture, then classify and tie them. The article titled" Evolution of the YOLO family of neural networks: from Variant 1 to variant 7 " provides a comprehensive overview of the development and improvement of the YOLO object detection framework. In the table below table 1 analysis of the research work and the stages of evolution of yolo:. YOLOvl Unified detection, single convolutional network First significant speedup in object detection Real-time detection, basic object tasks. YOLOv3 Deeper network, better feature extraction Improved performance in small objects and complex scenes Autonomous driving, security.
YOLOv5 Enhanced training, inference efficiency, developed by a different team Popular for practical applications due to ease of use and speed Widely adopted in real-time systems. YOLOv6 Focused on reducing complexity while maintaining performance Optimized for real-time applications Autonomous systems, edge devices. YOLOv7 Self-distillation, further architectural improvements Increased accuracy and efficiency, pushing the boundaries of real-time detection Latest in cutting-edge applications. The article provides a detailed overview of how Yolo has evolved over time, highlighting the main improvements and innovations in each version. It shows the progress of the platform from its initial release to the latest version of YOLOv7, showing how these continuous improvements have increased its performance, performance, and usability in real-time object detection scenarios[9].
We will focus on the ereshkels in labor :. R-CNN : relies on regional recommendations, but suffers from slow detection and high computational costs. YOLO : an innovative approach is to process the entire image as a single task, providing real-time performance with some compasses related to precision. Each algorithm provides a different balance between detection speed, accuracy, and calculation requirements. The article provides a comprehensive comparison showing the benefits of different uses, such as realtime applications in favor of YOLO compared to more specific but slower systems for R-CNN and its variants [10].
The use of YOLO in the parking meter project. The project provides for the identification and classification of parking spaces as empty or empty, based on data from cameras installed in the parking lot. The high speed of YOLO data processing ensures that the system can provide users with updates in real time, showing free parking spaces without significant delays. Implementation involves training the YOLO model based on a data set of parking images marked with the location of parking spaces and their working status. At the output, the model. The results then show which locations are present in the user interface for example, "empty: A5, C6". One of the tasks in this project is to accurately determine parking spaces in different lighting conditions, at different angles and in situations where other vehicles or objects cover them.
The reliability of YOLO and its ability to generalize in various situations make it suitable for solving this problem. In addition, it is possible to further improve the accuracy of the system by fine-tuning the model and using data augmentation techniques, which will allow reliable operation in the real world. The main performance indicators of object detection models are average accuracy mAP , merge intersection IoU , and output time.
These indicators will help you determine how well YOLOv8 detects and classifies parking spaces in real time. Average accuracy mAP : this indicator assesses the accuracy and responsiveness of the model within different reliability limits.
Ultralytics Глоссарий
In this paper, we present a detailed study of a real-time parking space detection system using neural networks. We address the problem of optimizing urban mobility through smart infrastructure, focusing on real-time object detection. The study emphasizes the importance of automating tasks requiring speed and accuracy, such as traffic and parking management. The purpose of the research is to develop an automated parking meter based on object detection algorithms, particularly the YOLO algorithm. Relevance of the study lies in the advanced object detection algorithms like YOLO , crucial for applications such as smart cities and surveillance systems.
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Systems for detecting fires are essential for protecting people and property. This study uses wireless sensor networks with deep learning to improve the accuracy of real-time fire detection systems and decrease false alarms. This model locates and classifies items quickly and precisely using deep learning techniques. To guarantee accurate detection, a sizable collection of fire-related data is used to train the model. When a fire occurs, users receive early warnings via WebRTC technology, and live footage of the burning location is broadcast. Using these sophisticated technologies, the efficiency of fire detection in the indoor environment can be improved, providing users with immediate and accurate alarms.
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