Vehicle Speed Measurement and Number Plate Detection using Real Time Embedded System

A real time system is proposed to detect moving vehicles that violate the speed limit. A dedicated digital signal processing chip is used to exploit computationally inexpensive image-processing techniques over the video sequence captured from the fixed position video camera for estimating the speed of the moving vehicles. The moving vehicles are detected by analysing the binary image sequences that are constructed from the captured frames by employing the inter-frame difference or the background subtraction techniques. The detected moving vehicles are tracked to estimate their speeds.This project deals with the tracking and following of single object in a sequence of frames and the velocity of the object is determined. The proposed method varies from previous existing methods in tracking moving objects, velocity determination and number plate detection. From the binary image generated, the moving vehicle is tracked using image segmentation of the video frames. The segmentation process is done by using the thresholding and morphological operations on the video frames. The object is visualized and its centroid is calculated. The distance it moved between frame to frame is stored and using this velocity is calculated with the frame rate of video.The images of the speeding vehicles are further analysed to detect license plate image regions. The entire simulation is done in matlab and simulink simulation software. Keywords:morphological;thresholding;segmentation;centroi

A multisensory monitoring and interpretation framework based on the model-view-controller paradigm

This paper proposes a monitoring and interpretation framework inspired in the Model?View?Controller (MVC) paradigm. Indeed, the paper proposes the extension of the traditional MVC paradigm to make it more flexible in incorporating the functionalities of a monitoring and interpretation system. The proposed model is defined as a hybrid distributed system where remote nodes perform lower level processing as well as data acquisition, while a central node is in charge of collecting the information and of its fusion. Firstly, the framework levels as well as their functionalities are described. Then, a fundamental part of the proposed framework, namely the common model, is introduced

Unlocking Solar Power For Surveillance A Review Of Solar Powered CCTV And Surveillance Technologies

Solar-powered surveillance technologies have gained prominence for their sustainable, autonomous, and versatile solutions. This comprehensive review explores three key solar-powered surveillance technologies: solar-powered CCTV cameras, solar drones, and solar-powered sensor networks. Each technology offers distinct strengths and weaknesses, making them suitable for various applications. Solar-powered CCTV cameras provide adaptability, energy independence, and rapid deployment, while solar drones offer an aerial perspective, extended endurance, and versatility. Solar-powered sensor networks excel in localized environmental monitoring. The choice of technology depends on factors such as the surveillance environment, budget constraints, required surveillance range, and specific monitoring needs. Organizations can benefit from hybrid solutions that integrate multiple technologies for comprehensive coverage. Future trends include advanced energy storage solutions, AI integration, enhanced power efficiency, and cloud-based data analytics, promising to improve performance and sustainability. Public-private collaborations and sustainable urban planning initiatives will drive further adoption and integration. Solar-powered surveillance technologies empower effective and environmentally sustainable surveillance solutions, contributing to a safer and more sustainable future

Pengukuran Kecepatan Kendaraan Menggunakan Optical Flow

Berdasarkan Undang-Undang Nomor 22 Tahun 2009 tentang Lalu Lintas dan Angkutan JalanPasal 287 ayat 5, setiap pengendara yang melanggar aturan batas kecepatan paling tinggi atau paling rendah akan dipidana. Upaya-upaya telah dilakukan untuk mengamati kecepatan kendaraan.Diantaranya pengukuran kecepatan kendaraan berbasis pengolahan citra digital.Pada Tugas Akhir ini pengukuran kecepatan kendaraannya akan memanfaatkan metode optical flow. Objek dari penelitian ini adalah video rekaman kendaraan di jalan raya.Pengukuran kecepatan kendaraan dilakukan beberapa langkah, yaitu; menentukan skala perbandingan, memilih area, memproses area terpilih dengan metodeImproved Three Frame Difference, menghitung nilaioptical flow dari objek pada area, dan menghitung kecepatan dari objek yang diperoleh dari nilai optical flow objek dikalikan dengan skala perbandingan. Dari uji coba yang sudah dilakukan didapatkan hasil bahwa metodeoptical flowHorn-Schunck mampu mendeteksi kecepatan kendaraan dengan tingkat akurasi paling tinggi 89,37%. =================================================================== Based on Law Number 22 Year 2009 on Road Tr affic and Transportation Clause 287 verse 5, every rider who violates the rules of the maximum speed limit or the lowest shall be punished. Attempts have been ma de to observe the speed of the vehicle. Among the measurement of vehicle speed based digital image processing. In this Final Project measurement of vehicle speed will utilize optical flow method. The object of this research is the video recording of vehicl es on the highway. Vehicle speed measurements performed several steps, namely; Determine the comparison scale, select the area, process the selected area by the Improved Three Frame Difference method, calculate the optical flow value of the object in the a rea, and calculate the velocity of the object obtained from the optical flow value of the object multiplied by the comparison scale. From the experiments that have been done the results obtained that the method of optical flow Horn - Schunck able to detect t he speed of the vehicle wi th the highest accuracy of 89,37 %

Ансамблевий класифікатор на основі бустінгу

Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету". Керівник роботи: д.т.н., професор, зав. кафедри авіаційних комп’ютерно-інтегрованих комплексів, Синєглазов Віктор МихайловичThis paper considers the construction of a classifier based on neural networks, nowadays AI is a major global trend, as an element of AI, as a rule, an artificial neural network is used. One of the main tasks that solves the neural network is the problem of classification. For a neural network to become a tool, it must be trained. To train a neural network you must use a training sample. Since the marked training sample is expensive, the work uses semi-supervised learning, to solve the problem we use ensemble approach based on boosting. Speaking of unlabeled data, we can move on to the topic of semi-supervised learning. This is due to the need to process hard-to-access, limited data. Despite many problems, the first algorithms with similar structures have proven successful on a number of basic tasks in applications, conducting functional testing experiments in AI testing. There are enough variations to choose marking, where training takes place on a different set of information, the possible validation eliminates the need for robust method comparison. Typical areas where this occurs are speech processing (due to slow transcription), text categorization. Choosing labeled and unlabeled data to improve computational power leads to the conclusion that semi-supervised learning can be better than teacher-assisted learning. Also, it can be on an equal efficiency factor as supervised learning. Neural networks represent global trends in the fields of language search, machine vision with great cost and efficiency. The use of "Hyper automation" allows the necessary tasks to be processed to introduce speedy and simplified task execution. Big data involves the introduction of multi-threading, something that large companies in the artificial intelligence industry are doing.У даній роботі розглядається побудова класифікатора на основі нейронних мереж, на сьогоднішній день AI є основним світовим трендом, як елемент AI, як правило, використовується штучна нейронна мережа. Однією з основних задач, яку вирішує нейронна мережа, є проблема класифікації. Щоб нейронна мережа стала інструментом, її потрібно навчити. Для навчання нейронної мережі необхідно використовувати навчальну вибірку. Оскільки позначена навчальна вибірка є дорогою, у роботі використовується напівконтрольоване навчання, для вирішення проблеми ми використовуємо ансамблевий підхід на основі бустингу. Говорячи про немарковані дані, ми можемо перейти до теми напівконтрольованого навчання. Це пов’язано з необхідністю обробки важкодоступних обмежених даних. Незважаючи на багато проблем, перші алгоритми з подібними структурами виявилися успішними в ряді основних завдань у додатках, проводячи експерименти функціонального тестування в тестуванні ШІ. Є достатньо варіацій для вибору маркування, де навчання відбувається на іншому наборі інформації, можлива перевірка усуває потребу в надійному порівнянні методів. Типовими областями, де це відбувається, є обробка мовлення (через повільну транскрипцію), категоризація тексту. Вибір мічених і немічених даних для підвищення обчислювальної потужності призводить до висновку, що напівкероване навчання може бути кращим, ніж навчання за допомогою вчителя. Крім того, воно може мати такий же коефіцієнт ефективності, як навчання під наглядом. Нейронні мережі представляють глобальні тенденції в області мовного пошуку, машинного зору з великою вартістю та ефективністю. Використання «Гіперавтоматизації» дозволяє обробляти необхідні завдання для впровадження швидкого та спрощеного виконання завдань. Великі дані передбачають впровадження багатопоточності, чим займаються великі компанії в індустрії штучного інтелекту

Інтелектуальна система виробництва друкованих плат

Робота публікується згідно наказу ректора від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт вищої освіти в репозиторії університету". Керівник дипломної роботи: д.т.н., проф., завідувач кафедри авіаційних комп’ютерно-інтегрованих комплексів, Синєглазов Віктор МихайловичThe main development of the project is the creation of an original, combined, automated system for the production of printed circuit boards in order to eliminate rejects and increase efficiency in time. Software development includes the operation of the YOLO neural network for improved detection and finding liquid and non-liquid areas with component clearance on the camera module under the control of the raspberry сontroller. Decision making takes place in a couple of steps, after which the machine understands what action should be taken, auto centering, reset, undo, etc. The practical significance of the work is the combination of several microcontrollers with the founder in the shop and improvement by means of new algorithms. Basic research has shown how switching from manual to automatic operation using remote communication can significantly reduce the time spent on core department processes. Using emulation with optional microcontroller connections, the problem of limited installer resources and the implementation of more complex algorithms in the installer's operation was solved. For greater project accuracy, the Mirae Mx-200 installer was considered, with statistics displayed for real projects. The physical and software connections allow the machine to be controlled remotely.Головною розробкою проекту є створення оригінальної, комбінованої, автоматизованої системи для виробництва друкованих плат з метою ліквідації браку та збільшення ефективності в часі. Розробка програмного забезпечення включає в себе роботу нейронної мережі YOLO для поліпшення задачі ідентифікування та знаходження ліквідних і неліквідних зон з компонентного просвічування на модулі камери під управлінням контролера расбері. Прийняття рішень після чого машина розуміє яку дію варто зробити, авто центрування, скидання, скасування і т.д. Практичним значенням роботи є комбінація декількох мікроконтролерів з установником в цеху та їх удосконалення шляхом нових алгоритмів. Основні дослідження показали як перехід з ручної роботи на автоматичну за допомогою віддаленого зв’язку можуть значно зменшити часові витрати на основні процеси департаменту. Використовуючи емуляцію з додатковим підключенням мікроконтролерів було вирішено проблему обмеженості ресурсів установника та впровадження більш складніших алгоритмів в його дію. Для більшої точності проекту, було розглянуто установник Mirae Mx-200, з відображенням статистики реальних проектів. Фізичне та програмне підключення дає змогу керувати машиною дистанційно

Solar-powered automated road surveillance system for speed violation detection

10.1109/TIE.2009.2038395IEEE Transactions on Industrial Electronics5793216-3227ITIE

Low-cost modular devices for on-road vehicle detection and characterisation

[EN] Detecting and characterising vehicles is one of the purposes of embedded systems used in intelligent environments. An analysis of a vehicle¿s characteristics can reveal inappropriate or dangerous behaviour. This detection makes it possible to sanction or notify emergency services to take early and practical actions. Vehicle detection and characterisation systems employ complex sensors such as video cameras, especially in urban environments. These sensors provide high precision and performance, although the price and computational requirements are proportional to their accuracy. These sensors offer high accuracy, but the price and computational requirements are directly proportional to their performance. This article introduces a system based on modular devices that is economical and has a low computational cost. These devices use ultrasonic sensors to detect the speed and length of vehicles. The measurement accuracy is improved through the collaboration of the device modules. The experiments were performed using multiple modules oriented to different angles. This module is coupled with another specifically designed to detect distance using previous modules¿ speed and length data. The collaboration between different modules reduces the speed relative error ranges from 1 to 5%, depending on the angle configuration used in the modules.This work was by the Spanish Science and Innovation Ministry: CICYT project PRESECREL: "Models and platforms for predictable, secure and reliable industrial information technology systems" PID2021-124502OB-C41. Funding for open access charge: CRUE-Universitat Politecnica de Valencia.Poza-Lujan, J.; Uribe-Chavert, P.; Posadas-Yagüe, J. (2023). Low-cost modular devices for on-road vehicle detection and characterisation. Design Automation for Embedded Systems. 27(1-2):85-102. https://doi.org/10.1007/s10617-023-09270-y85102271-2Broy M, Cengarle MV, Geisberger E (2012) Cyber-physical systems: imminent challenges. In: Monterey workshop, pp 1–28. SpringerWolf W (2009) Cyber-physical systems. IEEE Ann Hist Comput 42(03):88–89Serpanos D (2018) The cyber-physical systems revolution. Computer 51(3):70–73Hichri Y, Dahi S, Fathallah H (2021) Candidate architectures for emerging IOV: a survey and comparative study. Des Autom Embed Syst 25(4):237–263Canas V, Garcia A, de Las-Morenas J, Blanco J (2019) Modular and reconfigurable platform as new philosophy for the development of updatable vehicular electronics. Rev Iberoam de Automática e Inform Industr 16(2):200–211Asselin-Miller N, Biedka M, Gibson G, Kirsch F, Hill N, White B, Uddin K (2016) Study on the deployment of c-its in Europe: final report. Rep for DG MOVE MOVE/C 3:2014–794Corli A, Malaguti L (2021) Wave fronts in traffic flows and crowds dynamics. In: Cicognani M, Del Santo D, Parmeggiani A, Reissig M (eds) Anomalies in partial differential equations. Springer, Cham, pp 167–189Maity S, Bhattacharyya A, Singh PK, Kumar M, Sarkar R (2022) Last decade in vehicle detection and classification: a comprehensive survey. Arch Comput Methods Eng 28:1–38Sun Z, Bebis G, Miller R (2004) On-road vehicle detection using optical sensors: a review. In: Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No. 04TH8749), pp 585–590. IEEELi W, Li H, Wu Q, Chen X, Ngan KN (2019) Simultaneously detecting and counting dense vehicles from drone images. IEEE Trans Industr Electron 66(12):9651–9662Messoussi O, Magalhães FGd, Lamarre F, Perreault F, Sogoba I, Bilodeau G-A, Nicolescu G (2021) Vehicle detection and tracking from surveillance cameras in Urban scenes. In: International symposium on visual computing, pp 191–202. SpringerLozano Dominguez JM, Mateo Sanguino TJ (2019) Review on v2x, i2x, and p2x communications and their applications: a comprehensive analysis over time. Sensors 19(12):2756Qian X, Hao L (2020) Performance analysis of cooperative sensing over time-correlated Rayleigh channels in vehicular environments. Electronics 9(6):1004Hidalgo C, Marcano M, Fernandez G, Perez J (2020) Cooperative maneuvers applied to automated vehicles in real and virtual environments. Rev Iberoam de Autom e Inform industr 17(1):56–65Scaglia G, Serrano ME, Albertos Pérez P (2020) Control de trayectorias basado en álgebra lineal. Rev Iberoam de Autom e Inform industr 17(4):344–353Chen Z, Wu C, Huang Z, Lyu N, Hu Z, Zhong M, Cheng Y, Ran B (2017) Dangerous driving behavior detection using video-extracted vehicle trajectory histograms. J Intell Transport Syst 21(5):409–421Celik T, Kusetogullari H (2009) Solar-powered automated road surveillance system for speed violation detection. 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Int J Distrib Sens Netw 10(5):961073Li SE, Li G, Yu J, Liu C, Cheng B, Wang J, Li K (2018) Kalman filter-based tracking of moving objects using linear ultrasonic sensor array for road vehicles. Mech Syst Signal Process 98:173–189Jeon S, Kwon E, Jung I (2014) Traffic measurement on multiple drive lanes with wireless ultrasonic sensors. Sensors 14(12):22891–22906Stiawan R, Kusumadjati A, Aminah NS, Djamal M, Viridi S (2019) An ultrasonic sensor system for vehicle detection application. J Phys Conf Ser 1204:012017Marwedel P (2021) Embedded system design: embedded systems foundations of cyber-physical systems, and the internet of things. Springer, New YorkSwanson DC (2011) Signal Processing for intelligent sensor systems with MATLAB. CRC Press, Boca RatonBae I, Ji U (2019) Outlier detection and smoothing process for water level data measured by ultrasonic sensor in stream flows. 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Adv Math 136(1):15–25Lee M, Newman RE, Latchman HA, Katar S, Yonge L (2003) Homeplug 1.0 powerline communication lans-protocol description and performance results. Int J Commun Syst 16(5):447–473Poza-Lujan JL, Uribe-Chavert P, Sáenz-Peñafiel J-J, Posadas-Yagüe J-L (2021) Distributing and processing data from the edge. a case study with ultrasound sensor modules. Int Symp Distrib Comput Artif Intell 95:190–199Marko D, Hrubỳ D (2020) Distance measuring in vineyard row using ultrasonic and optical sensors. In: Proceeding of 22 Nd international conference of Young scientists. Praha: Česká Zemědělská univerzita, pp 194–204Semiconductors P (2000) The i2c-bus specification. Philips Semicond 9397(750):00954Huh J-H, Seo K (2017) An indoor location-based control system using Bluetooth beacons for IoT systems. Sensors 17(12):2917Gandhi MM, Solanki DS, Daptardar RS, Baloorkar NS (2020) Smart control of traffic light using artificial intelligence. In:2020 5th IEEE international conference on recent advances and innovations in engineering (ICRAIE), pp 1–6. IEEEUribe Chavert P, Gandhi MM (2022) Vehicle speed and length detector with ultrasound sensor and different angles. https://doi.org/10.5281/zenodo.7215268.‘github.com/puch18ou/

Primena inteligentnih sistema mašinske vizije autonomnog upravljanja železničkim vozilima

The railway is an important type of transport and has a significant economic impact on the industry and people's everyday life. Due to its capacities and complex infrastructure, it is necessary to work on its constant development and improvement. Railway automation requires the use of intelligent systems as a necessary part of an autonomous railway vehicle. As from the point of view of safe traffic, the existence of the object on the rail track and / or in its vicinity represents a potential obstacle to the railway traffic, and visibility has a very important role in correct and timely detection of the object on the railway infrastructure, a key element of autonomous railway vehicle is an obstacle detection system on the part of the railway infrastructure, in conditions of reduced visibility. The subject of scientific research of this doctoral dissertation is the application of intelligent machine vision systems in autonomous train operation. For the purpose of detecting obstacles on the part of the railway infrastructure in conditions of reduced visibility, a thermal imaging camera and a night vision system are integrated into the system, coupled with a developed advanced algorithm for image processing with artificial intelligence tools. In addition, the distance from the machine vision system to the detected object was estimated. The operation of the system was tested in a series of field experiments, at different locations, in different visibility conditions and weather conditions, through realistic scenarios