Raspberry Pi Vehicle Gateway System with Image Processing based Authorization Detection using IoT

Recognizing vehicle number plates is a difficult but much needed system. This is very useful for automating toll booths, automated signal breakers identification, finding out traffic rule breakers and to detect stolen vehicles. The developed system consist of a Raspberry Pi based vehicle number plate recognition system that automatically recognizes vehicle’s number plate using image processing. The system uses a digital camera interfaced to a Raspberry pi processor. A rear image of vehicle is captured and processed using various algorithms. The system constantly processes incoming camera footage to detect any trace of number plates. On sensing a number plate in front of the camera, it processes the camera input and extracts the number plate part from the image. The image is extracted using OCR. And by using CNN techniques, the number is extracted from the image. The system then displays the extracted number. The implemented system consist of a fully functional vehicle number plate recognition system using Raspberry Pi considering success rate and processing time as parameters. The developed system can be used for security purpose in housing societies to monitor the entry/exit of authorized vehicles. It is observed that the developed system successfully detects and recognizes the vehicle number plate on real time images. The system accuracy is about 80%

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

Tracking the stolen vehicles, parking vehicle identification and parking the vehicles in the restricted areas is a big challenge nowadays. The main way of addressing these challenges is to recognize them through the number of plates embedded with technology. The task is therefore to recognize the position and the character on the number plate of the car. The current research article proposed an innovative cost-effective dashboard to identify the number of plates by capturing the image using a camera that is plugged with Raspberry Pi. The character recognition on the number plate is done by using K-Nearest Neighbors (KNN) algorithm. The images of the number plate are captured using raspberry pi cam and the numbers from the plate are extracted using Open CV. These identified number of plates will be trained using machine learning (ML) models. Based on the mean and variance the Euclidian distance will be calculated for training purposes. After training the large available datasets with all the unique number plates the trained data will be in the form of some encrypted format. So by inputting the new image (number plate) the features will be extracted and based on the calculation of the distance the rank will be allocated with the new plate and gets the nearest plate with character recognition the number will be extracted.

Globally and locally, the number of vehicles is on the rise. It is becoming more and more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition to transportation systems automation. Where the extraction of the vehicle license plate is done without human intervention. Identifying the precise place of a vehicle through its license plate number from moving images of the vehicle image is among the crucial activities for vehicle plate discovery systems. Artificial intelligence systems are connecting the gap between the physical world and digital world of automatic license plate detection. The proposed research uses machine learning to recognizing Arabic license plate numbers. An image of the vehicle number plate is captured and the detection is done by image processing, character segmentation which locates Arabic numeric characters on a number plate. The system recognizes the license plate number area and extracts the plate area from the vehicle image. The background color of the number plate identifies the vehicle types: (1) White color for private vehicle; (2) red color for bus and taxi; (3) blue color for governmental vehicle; (4) yellow color for trucks, tractors, and cranes; (5) black color for temporary license; and (6) green color for army. The recognition of Arabic numbers from license plates is achieved by two methods as (1) Google Tesseract OCR based recognition and (2) Machine Learning-based training and testing Arabic number character as K-nearest neighbors (kNN). The system has been tested on 90 images downloaded from the internet and captured from CCTV. Empirical outcomes show that the proposed system finds plate numbers as well as recognizes background color and Arabic number characters successfully. The overall success rates of plate localization and background color detection have been done. The overall success rate of plate localization and background color detection is 97.78%, and Arabic number detection in OCR is 45.56 % as well as in KNN is 92.22%.

In simple terms, Automatic Vehicle Number Plate Recognition System (AVNPRS) can be described as a computer vision image processing technology. This technology captures images of vehicles and recognizes their number plates. As the world is becoming more sophisticated on each new day, security as well as management of transportation system have become a vital aspect in controlled places. Such places include modern campuses, shopping malls and other institutions. With today’s rate at which motor vehicles are increasing in numbers, there is need for an effective, affordable and efficient AVNPRS. Thus, this project proposes, outlines and discusses an efficient AVNPRS. The system under consideration in this project can be installed at main entrances of modern institutions. This is because the AVNPRS ensures that only authorized vehicles can automatically have access to such institutions. The AVNPRS ensures that it captures the image of the number plate once the input sensor detects the vehicle. The Sobel edge detection and Laplacian edge detection techniques are used at this critical stage. Thereafter, the Bounding box technique is used to find the number plate leading to character segmentation. After capturing, an image undergoes extraction and character investigation via the Optical Character Recognition (OCR). In addition, in achieving character recognition, matching between the computer template and segmented image is done via the OCR method. It is vital to mention that this system is sustainable as it successfully detects, recognizes and processes vehicle number plates on real images. The AVNPRS can be used for both traffic control and security. Therefore, the main aim of this piece of writing is to develop from a theoretical perspective an AVNPR system that can detect and capture vehicle number plate images.

The police forces around the world use vehicle number plate for legal vehicle authorization purposes, to check if a vehicle is registered or licensed. Most of us keep the vehicle papers in the vehicle itself, which is not at all safe in case of theft. In today's world, it is not secure to carry our vehicle papers and wherever we go. Hence, a system must be designed in which it is not necessary to carry our important documents to each and every place for verification. The aim is to design a system which captures the image of the number plate of a vehicle using a camera and the details are being retrieved using the character segmentation which is done by a feature extraction optical character recognition algorithm (OCR). Then the details retrieved from the number plate in text format is used to extract all the important information of the vehicle like, the name of the owner, address of the owner, date of registration of the vehicle etc. from the database. The police can verify whether the documents are fake or not. For us, it is useful as we do not have to carry our documents to every place with the fear of losing them.

Automatic Number Plate Recognition (LPR) plays an important role in numerous applications and various techniques have been proposed. In this paper we will learn about the automatic license plate detection of the security purpose and documentation of entry of vehicles inside any building. In this technique we are using easy-OCR for detection of the characters and numbers present in the number plate. By using this technique, we can store the number plate details and vehicle entry time in an accurate rate and in a faster way. In this project we are using the camera to take the picture of the number plate and then by using easy OCR we are taking the characters and number values from the image of the number plate and then the data is stored in the data base and can be exported when ever needed.

Automatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image , it display the number plate information into text. Mainly the ANPR system consists of 4 phases: Acquisition of Vehicle Image and PreProcessing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering , adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Sometimes the extracted plate area also contains noise, bolts, frames etc. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. This approach works well for low contrast, blurred, noisy as well as for dark and light/bright category images. The comparison is done between the ANPR with Adaptive Histogram Equalization and Iterative Bilateral Filtering that is the proposed approach and the existing ANPR approach using metrics: MSE, PSNR and Success rate.

In Parking within the premises, The focus of security is on the entry rather than the exit, which may become an advantage to thieves by increasing the chances of car theft. Where the registered vehicle can be stolen by simply stealing the entry card from the user which may cause a significant loss. The paper focuses on creating an automated and efficient system that prevents car theft by checking the vehicle and its owner during exit. This will be done through the combination of number plate detection and facial recognition technologies. The system will process the real-time data, The system will extract the image of the number plate and driver's face from the data. Then we will use OCR, the YOLO Algorithm, and OpenCV to detect the number plate characters and drivers‘ faces. Once the images are successfully recognized and matched from the registered database. The security system will allow the owner to safely exit. Otherwise, an alert will be sent to the owner and security. They can use this information to catch thieves and further stop theft. Keywords : OCR, YOLO, OpenCV Face-Recognition, Number Plate Detection

To design a system in order to identify an object number plate for the Indonesian format, an initial system is designed, in the form of a vehicle licence plate recognition application using template matching method. The goal of this application is to be implemented to the parking system by identifying the number plate. This system uses the camera for the image capture process, by utilizing image processing technology with the matching correlation template method for recognition to produce a string value from the image. Before doing recognition process, First, the pre processing stage is performed on the input image which includes grayscale, binary, until the segmentation stage before the correlation / comparison process is carried out on the image of Template. The process that occure in the pre-processing unit done for some reason including to make the image lighter and less complex. This process will make the image easer to be processed and also to increase the proses speed of the system. Before aply template matching algorithm to the image output from segmentation process, the image has to be resized first to match the size of the template image stored in data base. This has to done so that the target image and the template image can be match directly with template matching algorithm. The output of this system is a string value which is refer to the value of the license plate capture by camera used by the system. The problem that arises in the introduction process is how to identify various types of characters with various sizes and shapes so that the string value is the same as the text image. The average success rate of this application is 70% so that further research must be carried out so this system can be implemented into the parking system.
 Keyword : Image Processing, Template matching, Camera, Number Plate, Matlab

Automatic Number Plate Recognition (ANPR) is a technology that can be utilized by mall and movie theater parking management as well as toll booths on roads, expressways, etc. to expedite the toll collection process. Automated systems to retain vehicle information are becoming more and more important as the number of vehicles rises. It is imperative that communication be prioritized in order to effectively manage traffic and reduce crime. Number plate recognition is a dependable technique for automatic vehicle identification. With current algorithms, which are based on the concept of learning, developing adequate outcomes takes a long time and a lot of experience. An innovative use of machine learning is Automatic Number Plate Detection, which recognizes photos and transforms them into text. Using picture segmentation, the system locates, picks up, and extracts the vehicle's image and number plate. Afterwards, optical character recognition technology is used to identify characters in the retrieved image. This technology is used in military zones, apartment buildings, and traffic surveillance.

In most of the surveillance and restricted areas, vehicle recognition has become an essential task due to continuously increasing traffic. It is a very time consuming and tedious task for humans to manually keep a record of all the vehicles entering and leaving a particular area. Each vehicle has its distinctive number plate, which is the combination of special characters. With the help of these number plates, a training-based approach is followed in this paper to detect whether the entering vehicle is suspicious or not in particular area. The dataset of captured images of Indian vehicles is used for training using a deep learning approach. Here, all the images of dataset were trained on the YOLO-v4 object detection algorithm. The algorithm is tested on different vehicle images of different illuminations and variations of light. From the experimental results, this model is able to detect number plate with mean Average Precision (mAP) of 99.6%. Further, the recognition of number plates is done with the help of Optical Character Recognition (OCR) Engine. This system will allow automated entry, restricting suspicious and unwanted vehicles in the restricted areas. With the help of experimental results, we can evaluate the effectiveness of this model.

Technological developments have enabled the use of vehicle number plate recognition in various fields such as parking systems, e-toll payments, handling motor vehicle theft cases, traffic violations and so on. Vehicle number plate characteristics in each country have characteristics that are not the same. Each country has a standard numbering system in its writing method, colour, language and size of the license plate, so that to take the research standards for license plate numbers from other countries is not effective. Special research is needed in accordance with the format of the number plate used by the country, in this case Indonesia. This research applies simple method, morphological operations in detecting vehicle plates in Indonesia. Morphological results will be segmented to get the image of the plate character separately. The test results show that the method applied can provide a maximum accuracy of up to 100% on taking plate images with a distance of 1.5 meters.

The main aim of this work is to classify the plants according to its medicinal usage using real time processor Raspberry pi with the image of the leaf, as leaves play a major role for the classification of plants. Image of leaf is captured using digital camera of high resolution and is stored in memory card. Feature and pattern of the leaf are extracted using image processing technique which is then interfaced with Raspberry pi. Raspberry pi is stored with a program to classify the input image based on texture parameters of leaves. The quality and authenticity of the leaves are to be ensured for the preparation of herbal medicines. The image of medicinal plant leaves are thoroughly screened analyzed and compared with the database stored in Raspberry pi to give the correct measures of the texture of leaf with which we can find category to which the leaf belongs to. By applying advanced technique of image processing interfaced in Raspberry pi and utilizing the capabilities of the recent advanced computing and data/image storage facilities and the use of computer techniques, the above mentioned parameters are analyzed and compared with the database and classification of plant species is identified.

The process of detection and separation of yeast cells based on their morphological characteristics is critical to the understanding of cell division cycles, which is of vital importance to the understanding of some diseases such as cancer. The traditional process of manual detection is usually tedious and inconsistent. This paper presents a microfluidic device integrated with microvalves for fluid control for the sorting of yeast cells using image processing algorithms and confirmation based on their fluorescent tag. The proposed device is completely automated, low cost and easy to implement in an academic research setting. Design details of the integrated microfluidic system are highlighted in this paper, along with experimental validation. Real time cell sorting was demonstrated with a cell detection rate of 12 cells per minute.