scholarly journals An Illegal Dumping Detection System Based on Image Processing in OpenALPR

2021 ◽  
Vol 15 ◽  
pp. 1-7
Author(s):  
Wan Zakiah Wan Ismail
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Detection System ◽  
Social Economy ◽  
Raspberry Pi ◽  
License Plate ◽  
Microwave Radar ◽  
Plate Number ◽  
Illegal Dumping

Tipping or depositing large waste onto land using unauthorized and unlicensed methods are considered as illegal dumping. The increasing rate of illegal dumping becomes a crucial nation issue because this activity causes negative impacts to social, economy and environment. Thus, study on detecting the dumping activities is conducted to control the illegal dumping activities in Malaysia. Raspberry Pi with Python language is used as the microprocessor and a Raspberry Pi camera module with a microwave radar sensor are interfaced to it to capture the image of any vehicles entering the illegal dumping site. The image is captured to recognize the license plate of the vehicle. The method in this study is by using Open Automatic License Plate Recognition (ALPR), Open Computer Vision (CV) libraries and Optical Character Recognition (OCR) to detect the character of the plate registration number. The outcome of the study consists of recognition of Malaysia vehicles’ plate number and the automatic real time email notification on the illegal dumping case. The detection system can be used for case monitoring since the plate number recognition is done in real time. The system can be upgraded to ensure its sustainability in the harsh and isolated environment.

scholarly journals Detection of Non-Helmet Riders and Extraction of License Plate Number using Yolo v2 and OCR Method

2019 ◽  
Vol 9 (2) ◽  
pp. 5167-5172
Keyword(s):  
Object Detection ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Detection System ◽  
Research Work ◽  
License Plate ◽  
Plate Number ◽  
Traffic Violations ◽  
Registration Number ◽  
Traffic Violation

In current situation, we come across various problems in traffic regulations in India which can be solved with different ideas. Riding motorcycle/mopeds without wearing helmet is a traffic violation which has resulted in increase in number of accidents and deaths in India. Existing system monitors the traffic violations primarily through CCTV recordings, where the traffic police have to look into the frame where the traffic violation is happening, zoom into the license plate in case rider is not wearing helmet. But this requires lot of manpower and time as the traffic violations frequently and the number of people using motorcycles is increasing day-by-day. What if there is a system, which would automatically look for traffic violation of not wearing helmet while riding motorcycle/moped and if so, would automatically extract the vehicles’ license plate number. Recent research have successfully done this work based on CNN, R-CNN, LBP, HoG, HaaR features,etc. But these works are limited with respect to efficiency, accuracy or the speed with which object detection and classification is done. In this research work, a Non-Helmet Rider detection system is built which attempts to satisfy the automation of detecting the traffic violation of not wearing helmet and extracting the vehicles’ license plate number. The main principle involved is Object Detection using Deep Learning at three levels. The objects detected are person, motorcycle/moped at first level using YOLOv2, helmet at second level using YOLOv3, License plate at the last level using YOLOv2. Then the license plate registration number is extracted using OCR (Optical Character Recognition). All these techniques are subjected to predefined conditions and constraints, especially the license plate number extraction part. Since, this work takes video as its input, the speed of execution is crucial. We have used above said methodologies to build a holistic system for both helmet detection and license plate number extraction.

scholarly journals A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 55
Author(s):  
Nicole do Vale Dalarmelina ◽  
Marcio Andrey Teixeira ◽  
Rodolfo I. Meneguette
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Transportation Systems ◽  
Identification System ◽  
License Plate ◽  
Optical Character ◽  
Smart Parking

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

scholarly journals Vehicle Number Plate Recognition using Micro Processor

2019 ◽  
Vol 8 (12) ◽  
pp. 3682-3684
Keyword(s):  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Processing Technology ◽  
License Plate ◽  
Text Format ◽  
Optical Character ◽  
Plate Number ◽  
Image Processing Technology

An automatic license number plate recognition system that uses image processing technology for identifying the written characters and numbers on the vehicle’ license plate. The system can be used in highly secured areas to provide more safety, and can be used in parking, traffic, and other places to monitor all vehicle’s number plate in a predefined area. The character is recognized by the OCR technology that is optical character recognition system. It generates the vehicle’s license plate number in a text format. The recognized number from the license plate then can be used to retrieve more information about the vehicle and the owner.

scholarly journals AUTOMATICS NUMBER PLATE RECOGNITION USING CONVOLUTION NEURAL NETWORK

2020 ◽  
Vol 3 (2) ◽  
pp. 234-244
Author(s):  
Siddhartha Roy ◽  
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Rapid Development ◽  
Red Light ◽  
Training Needs ◽  
Character Segmentation ◽  
License Plate ◽  
Training Set ◽  
Optical Character

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used for security, safety, and also commercial aspects such as parking control access, and legal steps for the red light violation, highway speed detection, and stolen vehicle detection. The license plate of any vehicle contains a number of numeric characters recognized by the computer. Each country in the world has specific characteristics of the license plate. Due to rapid development in the information system field, the previous manual license plate number writing process in the database is replaced by special intelligent device in a real-time environment. Several approaches and techniques are exploited to achieve better systems accuracy and real-time execution. It is a process of recognizing number plates using Optical Character Recognition (OCR) on images. This paper proposes a deep learning-based approach to detect and identify the Indian number plate automatically. It is based on new computer vision algorithms of both number plate detection and character segmentation. The training needs several images to obtain greater accuracy. Initially, we have developed a training set database by training different segmented characters. Several tests were done by varying the Epoch value to observe the change of accuracy. The accuracy is more than 95% that presents an acceptable value compared to related works, which is quite satisfactory and recognizes the blurred number plate.

scholarly journals Aplikasi Pendeteksi Plat Nomor Kendaraan Berbasis Raspberry Pi Menggunakan Website Untuk Pelanggaran Lalu Lintas

2019 ◽  
Vol 11 (2) ◽  
pp. 84-89
Author(s):  
Siska Aulia ◽  
Popy Maria ◽  
Ramiati Ramiati
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Raspberry Pi ◽  
License Plate ◽  
Long Distance ◽  
The Public ◽  
Segmentation Methods ◽  
Traffic Violations ◽  
Motorized Vehicles ◽  
Wheeled Vehicles

Motorized vehicles in Indonesia consist of two-wheeled vehicles and four-wheeled vehicles. The number of motorized vehicles is increasing every year. The higher the vehicle volume the higher the level of traffic violations. Every violator will be charged a ticket by the ticketing officer if the vehicle user does not obey the driving rules. The ticketing process in Indonesia is still manually using paper by writing violations committed by violators on a piece of paper. This article is an attempt to make it easier for the public and police in traffic violations. This article is designed for vehicle license plate detection applications and traffic violation websites. The plate identification process begins with taking a plate image through a Raspberry Pi-based camera or webcam. The plate image results using the Raspberry Pi camera are carried out by processing the vehicle plate digital image by segmentation methods and Optical Character Recognition (OCR) using matlab. The vehicle plate character results obtained are used as input to identify traffic violations. The form of traffic violations can be seen on the traffic ticket website. Based on the results of OCR testing proved to be able to recognize the image of the vehicle plate. Raspberry Pi based camera for long distance or wireless communication. The results from the traffic ticket website are used as evidence to process motorists who have violated traffic.  

scholarly journals Real-time Automated Detection and Recognition of Nigerian License Plates via Deep Learning Single Shot Detection and Optical Character Recognition

2021 ◽  
Vol 14 (4) ◽  
pp. 11
Author(s):  
Kayode David Adedayo ◽  
Ayomide Oluwaseyi Agunloye
Keyword(s):  
Real Time ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Character Segmentation ◽  
Detection Accuracy ◽  
License Plate ◽  
Single Shot ◽  
Optical Character ◽  
License Plate Detection ◽  
Detection And Recognition

License plate detection and recognition are critical components of the development of a connected Intelligent transportation system, but are underused in developing countries because to the associated costs. Existing license plate detection and recognition systems with high accuracy require the usage of Graphical Processing Units (GPU), which may be difficult to come by in developing nations. Single stage detectors and commercial optical character recognition engines, on the other hand, are less computationally expensive and can achieve acceptable detection and recognition accuracy without the use of a GPU. In this work, a pretrained SSD model and a tesseract tessdata-fast traineddata were fine-tuned on a dataset of more than 2,000 images of vehicles with license plate. These models were combined with a unique image preprocessing algorithm for character segmentation and tested using a general-purpose personal computer on a new collection of 200 automobiles with license plate photos. On this testing set, the plate detection system achieved a detection accuracy of 99.5 % at an IOU threshold of 0.45 while the OCR engine successfully recognized all characters on 150 license plates, one character incorrectly on 24 license plates, and two or more incorrect characters on 26 license plates. The detection procedure took an average of 80 milliseconds, while the character segmentation and identification stages took an average of 95 milliseconds, resulting in an average processing time of 175 milliseconds per image, or 6 photos per second. The obtained results are suitable for real-time traffic applications.

scholarly journals Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 555
Author(s):  
Jui-Sheng Chou ◽  
Chia-Hsuan Liu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Character Recognition ◽  
Initial Period ◽  
Control Point ◽  
Recognition Rate ◽  
Classification Model ◽  
License Plate ◽  
Construction Site ◽  
Illegal Mining

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

scholarly journals Residential Area Surveillance and Parking System

2020 ◽  
Vol 9 (5) ◽  
pp. 602-606
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Residential Area ◽  
High Rate ◽  
Identification System ◽  
License Plate ◽  
License Plate Recognition ◽  
Position Effects ◽  
Plain Text ◽  
Alert Message

The vehicles playing the vital role in our day to day life for transport, and some of the vehicles violates the traffic rules are also increasing, vehicle theft, unnecessary entering into highly restricted areas, increased number of accidents lead to increase in the rate of crime slowly. The vehicle had its own identity it should be recognized which plays the major role in the world. For recognition of the vehicles which are used commonly in the field of safety and security system, LPDR plays a major role and the vehicle registration number is recognized at some certain distance accurately. License Plate recognition is the most efficient and cost effective technique used for detection and recognition purposes. Automatic license plate recognition (ALPR) is used for finding the location of the license plate in the vehicle. These methods and techniques vary based on the conditions like, quality of the image, vehicle on a fine-tuned position, effects of lighting, type of image, etc. The objective is to design an efficient automatic conveyance identification system of sanctioned or unauthorized in the residential societies by utilizing the conveyance number plate. By getting the car image from the surveillance camera in the entrance, we recognizing the number plate and the characters are extracted using OCR (optical character recognition). It converts the character in the image to plain text. Then the plain text of the license plate is cross-verified with the database to check whether the vehicle belongs to residents or visitor. It sends the alert message to the security official when a new visitor request method in a residential area. The log details are stored separately for the resident and visitor in the database. It also provides the details about the parking area availability in the residential area. By calculating the number of vehicles in and out of the area, the detail or availability parking slot is displayed and it sis robust to the size, lighting effects with high rate of detection.

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