Real-Time Vision System for License Plate Detection and Recognition on FPGA

Rapid development of the Field Programmable Gate Array (FPGA) offers an alternative way to provide acceleration for computationally intensive tasks such as digital signal and image processing. Its ability to perform parallel processing shows the potential in implementing a high speed vision system. Out of numerous applications of computer vision, this paper focuses on the hardware implementation of one that is commercially known as Automatic Number Plate Recognition (ANPR).Morphological operations and Optical Character Recognition (OCR) algorithms have been implemented on a Xilinx Zynq-7000 All-Programmable SoC to realize the functions of an ANPR system. Test results have shown that the designed and implemented processing pipeline that consumed 63 % of the logic resources is capable of delivering the results with relatively low error rate. Most importantly, the computation time satisfies the real-time requirement for many ANPR applications

Automatic Number Plate Recognition on FPGA

Automatic Number Plate Recognition (ANPR) systems have become one of the most important components in the current Intelligent Transportation Systems (ITS). In this paper, a FPGA implementation of a complete ANPR system which consists of Number Plate Localisation (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR) is presented. The Mentor Graphics RC240 FPGA development board was used for the implementation, where only 80% of the available on-chip slices of a Virtex-4 LX60 FPGA have been used. The whole system runs with a maximum frequency of 57.6 MHz and is capable of processing one image in 11ms with a successful recognition rate of 93%

Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods

Optical character recognition on heterogeneous SoC for HD automatic number plate recognition system

Automatic number plate recognition (ANPR) systems are becoming vital for safety and security purposes. Typical ANPR systems are based on three stages: number plate localization (NPL), character segmentation (CS), and optical character recognition (OCR). Recently, high definition (HD) cameras have been used to improve their recognition rates. In this paper, four algorithms are proposed for the OCR stage of a real-time HD ANPR system. The proposed algorithms are based on feature extraction (vector crossing, zoning, combined zoning, and vector crossing) and template matching techniques. All proposed algorithms have been implemented using MATLAB as a proof of concept and the best one has been selected for hardware implementation using a heterogeneous system on chip (SoC) platform. The selected platform is the Xilinx Zynq-7000 All Programmable SoC, which consists of an ARM processor and programmable logic. Obtained hardware implementation results have shown that the proposed system can recognize one character in 0.63 ms, with an accuracy of 99.5% while utilizing around 6% of the programmable logic resources. In addition, the use of the heterogenous SoC consumes 36 W which is equivalent to saving around 80% of the energy consumed by the PC used in this work, whereas it is smaller in size by 95%

Implementation of License Plate Recognition Monitoring System using Neural Network on Solar Powered Microcontroller

One automatic system for monitoring the presence of vehicles in a parking zone is an indispensable mean of an area such as services building, institutions, and other organizations, which accomodated many vehicles. A tracking record is the most important matter when the vehicle leaves the parking area. Manually, one parking officer will be needed to do the job. However, when using such an automated parking system, then this officers' job can be replaced. The vehicle license plate recording system is designed to use electronic-vision devices as a fundamental device for detecting the presence of vehicle. Vehicle license plates are detected using a digital camera which captured by the camera module on the Raspberry-Pi mini-pc microcontroller device, in addition to the detection of ultrasonic sensors that capture the position of vehicle objects. The process of reading vehicle numbers in an intelligent system of artificial neural networks to extracts each character of the license plate so that each number and letter can be recognized. Meanwhile, the detection of the ultrasonic parking sensor is a complementary confirmation indicating the presence of a vehicle object being monitored. The combination of solar power as the power supply for this automatic system is an important set-up that makes the system's electricity able to run independently. This monitoring system is prepared to help increase vehicle security automatically

Pay as You Go: A Generic Crypto Tolling Architecture

The imminent pervasive adoption of vehicular communication, based on dedicated short-range technology (ETSI ITS G5 or IEEE WAVE), 5G, or both, will foster a richer service ecosystem for vehicular applications. The appearance of new cryptography based solutions envisaging digital identity and currency exchange are set to stem new approaches for existing and future challenges. This paper presents a novel tolling architecture that harnesses the availability of 5G C-V2X connectivity for open road tolling using smartphones, IOTA as the digital currency and Hyperledger Indy for identity validation. An experimental feasibility analysis is used to validate the proposed architecture for secure, private and convenient electronic toll payment

Development of Automatic Digitization of Truck Number in Open Cast Mines Using Microcontroller

Geological condition in mines appears to be extremely complicated and there are many intelligence security problems. Production is falsely transfer by the unauthorized truck from mine pits also at loading point. It also lifted in wrong ways by malfunctioning of the truck weight in Weigh Bridge. Mining organizations are under the control of mafia and countless can be added to the mines mafia. An intelligence security system is need to monitor truck number in automatically using image acquisition method, automatic detection, recognition process, communication technology, information technology and microcontroller innovation to understand the working specification of the mining region. Tracking of the number plate from the truck is an important task, which demands intelligent solution. Intelligent surveillance in open casts mine security network using data accession is a prime task that protects the secure production of mines. So automatic truck number recognition technique is used to recognize the registration number of the truck which is used for transferring the mine production as well as track record the amount of the production. It also preserves the mines and thus improving its security. For extraction and recognition of number plate from truck image the system is uses MATLAB software tool. It is assumed that images of the truck have been captured from digital camera. The data acquisition terminal uses the PIC16F877A microcontroller as a core chip for sending data. The data are communicated through USB to TTL converter (RS232) with the main circuit to realize intelligent monitoring. To store the data in permanently it is uses EEPROM chip. Alphanumeric Characters on plate has been extracted and recognized using template images of alphanumeric characters. The proposed system performs the real time data monitoring to recognize the registration number plate of the trucks for getting required important information. It also provides to maintenance the history of data and support access contro

Intraframe Scene Capturing and Speed Measurement Based on Superimposed Image: New Sensor Concept for Vehicle Speed Measurement

A vision based vehicle speed measurement method is presented in this paper. The proposed intraframe method calculates speed estimates based on a single frame of a single camera. With a special double exposure, a superimposed image can be obtained, where motion blur appears significantly only in the bright regions of the otherwise sharp image. This motion blur contains information of the movement of bright objects during the exposure. Most papers in the field of motion blur are aiming at the removal of this image degradation effect. In this work, we utilize it for a novel speed measurement approach. An applicable sensor structure and exposure-control system are also shown, as well as the applied image processing methods and experimental results. © 2016 Mate Nemeth and Akos Zarandy