Plate Detection Research Articles

Exploration and Application of Acoustic Emission Detection Technology in Leakage Detection of the Bottom Plate of Crude Oil Metal Storage Tanks

The use of acoustic emission detection technology can conduct online and non-stop detection and evaluation of crude oil metal storage tanks, achieving the integrity detection and evaluation of storage tanks. In order to explore the applicability of acoustic emission detection technology in the leakage detection of the bottom plate of crude oil metal storage tanks, this project carried out online acoustic emission detection of crude oil metal storage tanks, magnetic flux leakage detection after tank opening and cleaning, and compared and analyzed the results of two detection technologies. The adaptability of online acoustic emission detection technology was analyzed and explored. It was found that acoustic emission technology can qualitatively evaluate the corrosion of tank bottom plates, but cannot accurately locate corrosion defects. For tanks with severe corrosion that require maintenance, it is still necessary to open the tank and use magnetic flux leakage and other detection techniques to determine the location of defects. Through comparative analysis of acoustic emission testing technology and magnetic flux leakage testing technology, the results obtained from acoustic emission testing technology are conservative compared to the actual results. It is recommended to upgrade the evaluation of corrosion degree for the acoustic emission evaluation results of crude oil metal storage tanks.

LICENSE PLATE DETECTION USING MACHINE LEARNING

A crucial technology with many applications in traffic management, security, and law enforcement is license plate detection utilizing machine learning. In this abstract, a machine learning-based method for precisely locating and identifying license plates in pictures and video streams is presented. Data gathering, preprocessing, object detection using deep learning methods like YOLO, license plate recognition using optical character recognition (OCR), and post-processing to improve findings are all part of the mPlate Detection, OpenCV module, OCR Technology.ethodology. Metrics including precision, recall, F1-score, are used to assess performance. The system offers enormous promise for improving efficiency and safety in numerous sectors when it is implemented in real-world circumstances, whether on edge devices or in the cloud. This abstract emphasizes how important machine learning is for automating difficult processes, solving real-world problems, and improving CV. The trained model can also be used in a variety of applications, including security systems, toll booth monitoring, parking enforcement, traffic management systems, and parking enforcement. The system can be implemented on edge devices for real-time processing or in the cloud for scalability depending on the individual requirements of the application.

VEHICLE LICENSE PLATE DETECTION USING YOLO ALGORITHM

<p><em>Urban population growth has created challenges in efficient parking space management. Manual data collection is time-consuming and error-prone, especially at night. Modern technology-based solutions are urgently needed. This research focuses on an innovative parking management system using YOLO for real-time object detection, including license plates. The objective is to assess the YOLO algorithm's accuracy in license plate detection. The methodology follows software development best practices, utilizing Python and Tkinter GUI for an intuitive interface. YOLO and EasyOCR enable object detection and character recognition. Results show high accuracy: 88.8% for HD and 86.3% for sub-HD resolutions. YOLO proves reliable for license plate data collection, reducing manual intervention and enhancing parking management.</em></p>

Automatic Jordanian License Plate Detection and Recognition System Using Deep Learning Techniques.

Recently, the number of vehicles on the road, especially in urban centres, has increased dramatically due to the increasing trend of individuals towards urbanisation. As a result, manual detection and recognition of vehicles (i.e., license plates and vehicle manufacturers) become an arduous task and beyond human capabilities. In this paper, we have developed a system using transfer learning-based deep learning (DL) techniques to identify Jordanian vehicles automatically. The YOLOv3 (You Only Look Once) model was re-trained using transfer learning to accomplish license plate detection, character recognition, and vehicle logo detection. In contrast, the VGG16 (Visual Geometry Group) model was re-trained to accomplish the vehicle logo recognition. To train and test these models, four datasets have been collected. The first dataset consists of 7035 Jordanian vehicle images, the second dataset consists of 7176 Jordanian license plates, and the third dataset consists of 8271 Jordanian vehicle images. These datasets have been used to train and test the YOLOv3 model for Jordanian license plate detection, character recognition, and vehicle logo detection. In comparison, the fourth dataset consists of 158,230 vehicle logo images used to train and test the VGG16 model for vehicle logo recognition. Text measures were used to evaluate the performance of our developed system. Moreover, the mean average precision (mAP) measure was used to assess the YOLOv3 model of the detection tasks (i.e., license plate detection and vehicle logo detection). For license plate detection, the precision, recall, F-measure, and mAP were 99.6%, 100%, 99.8%, and 99.9%, respectively. While for character recognition, the precision, recall, and F-measure were 100%, 99.9%, and 99.95%, respectively. The performance of the license plate recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 99.8%, 99.8%, and 99.8%, respectively. Furthermore, for vehicle logo detection, the precision, recall, F-measure, and mAP were 99%, 99.6%, 99.3%, and 99.1%, respectively, while for vehicle logo recognition, the precision, recall, and F-measure were 98%, 98%, and 98%, respectively. The performance of the vehicle logo recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 95.3%, 99.5%, and 97.4%, respectively.

A Design Space Exploration of Power-efficient Gaussian Filter Architectures using Logical Optimization and Approximated Adders

Gaussian filtering is an important step in many image-processing applications because it reduces image noise.However, this step is also compute-intensive, so power-optimized hardware architectures are necessary to allow its adoption in embedded devices. This work presents a design space exploration of power-efficient Gaussian filter architectures.Differently from related work, this work shows the impacts of the design decisions on two target applications: the Canny Algorithm and an Automatic License Plate Recognition system.To explore the design space, the 3x3, 5x5, and 7x7kernels were logically refactored using Multiplierless Constant Multiplication and Common Sub-expression Exploration. The adders were approximated using the copy strategy to further reduce power consumption. Systematic experiments show the effects of the adopted strategies on power savings and quality of results compared to exact baselines using the two applications.The approximate strategy reached up to 51 dB of Peak Signal-to-Noise Ratio with power reductions of up to 48% in the best-case scenario of the standalone Gaussian filters. Also, the total power of the Gaussian filter in the Canny Algorithm can be reduced down to 34% while maintaining the precision of results between 57% and 90%. Finally, the proposed strategies reduce up to 64% of the Gaussian filter power consumption when adopted in the plate detection solution with a similar detection rate compared to the exact filter architecture.

Generates Warning Signals Based on Number Plate Recognition to Prevent Parking Violations

Parking violations are very frequent around us, especially in urban areas, which greatly disturbs other road users so the government imposes strict sanctions on parking violations. To solve this problem, a surveillance system is needed to immediately alert drivers to move their vehicles to the correct parking space. In this paper, a monitoring system for parking violations has been developed that focuses on carrying out license plate recognition on cars and giving warnings to drivers to move their cars from the wrong parking location via voice media. The steps involved in license plate recognition are license plate detection, character segmentation, and character recognition by performing OCR (Optical Character Recognition). Furthermore, the license plate recognition results will be used as input for sound generation as a warning. In this paper, a GUI was also developed to help users run programs that have been developed. Finally, the experimental results show that a simple monitoring system to alert parking violations can be established.

LICENSE PLATE RECOGNITION SYSTEM BASED ON MASK R-CNN

Automatic license plate recognition (ALPR) systems can be found in many different applications. If a person has a driving license, the person probably had already seen smart road camera after a speed limit sign or on a crossroad. Every year number of cars on roads growth very fast. It is also obvious that such systems can be used out-of-road situations. For instance, this type of systems can be used for automatic access control on private property or smart parking, or even log system that are being used literally everywhere. Because of popularity of ALPR systems, there are two main goals, which are being pursued by researches: speed and accuracy of recognition. Speed of the detection is important for real-time systems. Accuracy is important for every system. The more accurate a system is, the more reliable it is. For example, car accident detection systems should be as accurate as possible in order to be used, because no one wants to get billed with the wrongdoing, that wasn’t committed by them.
 The purpose of the study is to develop high precision automatic license plate detection system with number extraction possibilities. In order to achieve the goal many different modern solutions and technologies were studied and solution is presented. The main technology of the project is artificial intelligence system and, more specifically, convolutional neural network. As the main algorithm Mask R-CNN is used for license plate detection. To present reasonable research, the system was tested on different hardware (CPU, GPU, Raspberry PI 4) and on different datasets.

MVSR Normalization Algorithm Method for Improving Vehicle License Plate Recognition

Image processing and embedded systems are used in many applications such as object recognition, robotic applications, autonomous and remote control systems developed for the defense industry, medical applications, face recognition, and vehicle license plate recognition. Many vehicle license number plate detection methods are not effective under vehicle license plate images have a degree of rotation or low resolution images. Thus, we used MVSR normalization algorithm to detect vehicle license plate recognition for better accuracy and lower computational cost. The MVSR normalization algorithm, Mean–Variance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.

Vibration based damage detection of steel plate

It is very important to monitor the occurrence of damage of various structures. By early detection of damage and by taking the proper corrective measures the failure of damage could be avoided. When damage occurs in structure it results in change in stiffness properties of the structure which in turn result in change in modal properties like natural frequency, mode shape change, mode shape curvature and strain energy of a structure. These parameters are used as an input to train the artificial neural network(ANN). The efficiency of the ANN to predict the damage at single and multiple damage cases using the change in natural frequency and strain energy was evaluated in this study. The efficiency of individual modes and combination of frequency and strain energy to detect damage is also considered in this study. The modal parameters were obtained by carrying out modal analysis using ANSYS 2021 R1 software and the ANN was developed using MATLAB software. The results indicated that the strain energy accurately predict the damage location and varies linearly with severity of damage. It is better to use combination of frequency and strain energy as indicator to detect damages.

Number Plate Detection in an Image

Automatic Vehicle license plate 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 license plate detection and recognitions. Due to the varying characteristics of the license plate like numbering system, colors, style and sizes of license plate, When detection and recognition are two separate jobs, which also results in a huge number of factors, there is an issue with identification. So,further research is still needed in this area. We propose a unified convolutional neural network (CNN) and the F1 score as metrics in a deep learning project for picture categorization which can localize license plates and recognize the letters. We work on license plate recognition and segments characters in the license plate firstly, and then recognizes each segmented character using Optical Character Recognition(OCR)techniques. Extensive experiments show the effectiveness and the efficiency of our proposed approach.

ADD: An automatic desensitization fisheye dataset for autonomous driving

Autonomous driving systems require many images for analyzing the surrounding environment. However, there is fewer data protection for private information among these captured images, such as pedestrian faces or vehicle license plates, which has become a significant issue. In this paper, in response to the call for data security laws and regulations and based on the advantages of large Field of View (FoV) of the fisheye camera, we build the first Autopilot Desensitization Dataset, called ADD, and formulate the first deep-learning-based image desensitization framework, to promote the study of image desensitization in autonomous driving scenarios. The compiled dataset consists of 650K images, including different face and vehicle license plate information captured by the surround-view fisheye camera. It covers various autonomous driving scenarios, including diverse facial characteristics and license plate colors. Then, we propose an efficient multitask desensitization network called DesCenterNet as a benchmark on the ADD dataset, which can perform face and vehicle license plate detection and desensitization tasks. Based on ADD, we further provide an evaluation criterion for desensitization performance, and extensive comparison experiments have verified the effectiveness and superiority of our method on image desensitization.

License plate recognition in slow motion vehicles

The recognition of license plate numbers represents one of the most efficient techniques to identify any individual vehicle. The principle of the system is that the detection of the license plate will be done with two techniques first you only look once (YOLO) and cascade classifier. Then after achive correct detection, the system will send the result (the image of the license plate) to Easy optical character recognition (OCR) library to read it and transform the image into text. In this paper, an analytical study of the surveillance system which affects by parallax due to camera movement has been done, by merging the OCR technique with the attached camera using python aided Raspberry Pi. The hardware system has been designed and implemented.

License plate recognition in slow motion vehicles

The recognition of license plate numbers represents one of the most efficient techniques to identify any individual vehicle. The principle of the system is that the detection of the license plate will be done with two techniques first you only look once (YOLO) and cascade classifier. Then after achive correct detection, the system will send the result (the image of the license plate) to Easy optical character recognition (OCR) library to read it and transform the image into text. In this paper, an analytical study of the surveillance system which affects by parallax due to camera movement has been done, by merging the OCR technique with the attached camera using python aided Raspberry Pi. The hardware system has been designed and implemented.

Studies Advanced in License Plate Recognition

License plate recognition is a crucial mission in computer vision, and deep learning has significantly improved its performance. A representative license plate recognition system involves five components: license plate image preprocessing, image acquisition, license plate detection, character recognition, and character segmentation. This paper will explore the methods commonly used in each stage of the recognition process and analyze some of the current challenges and future trends of license plate recognition. This discussion will consider real-world factors such as lighting and weather conditions that can affect recognition accuracy. Ultimately, it is hoped that these insights will contribute to the development of intelligent transportation systems.

Boosting One-Stage License Plate Detector via Self-Constrained Contrastive Aggregation

Scene Text Detection (STD) has applied in many fields successfully. One of the important applications of STD is License Plate Detection (LPD). As a unique identity of vehicle, License Plate (LP) facilitates the intelligent transportation in many fields, such as traffic enforcement, intelligent transportation dispatching, etc. However, there are many scene texts similar to LPs causing misjudgment of LP detector. To alleviate these disturbances, more discriminative features are necessary. In latent feature space, discriminative features should aggregate into a tight cluster to widen decision boundary. We assume three perspectives about how to aggregate features and boost feature expression. From these assumptions, a special contrastive triad is designed. Then, we propose a Self-Constrained Contrastive Aggregation (SCCA) method to lead the feature aggregation in latent space and boost the feature expression of backbone. The proposed SCCA is jointly trained with supervised learning for detection to improve the detection performance. The experiments show that our proposed SCCA prompts the baseline significantly and exceeds recent LP detectors, reaching 99.7 on both F1-score and AP on UFPR-ALPR dataset. Meanwhile, we compare the self-constrained contrastive learning with vanilla contrastive learning in experiments and visualize their LP features. The results show that our proposed SCCA reaches better performance and verifies our assumptions are reasonable.

A GPU-free license plate detection based on fused color-edge and Retina approach

A GPU-free license plate detection based on fused color-edge and Retina approach

IUKL Smart Car Parking App Using Automatic License Plate Recognition and Raspberry Pi

This Paper outlines the development of the IUKL Smart Parking mobile app developed for the Infrastructure University of Kuala Lumpur, Malaysia. The Android mobile application was developed using android studio and allows users to make reservations for the IUKL campus parking facilities. Furthermore, an automatic license plate recognition algorithm was developed using a Raspberry Pi computer and tested using sample license plate images displayed on a smartphone. The license plate detection algorithm was implemented using Python as the programming language and the Python library Python-tesseract, which is an optical character recognition tool for Python. The automatic license plate recognition technique was created using this Python module. Additionally, the algorithm was tested using sample license plate images and this was accomplished using images displayed from a mobile device and yielded mainly accurate results, though not 100%. The results were displayed by emitting different LED colored lights, “Red” was emitted when a plate number was not identified, and “Green” was emitted when the plate number was identified successfully. Moreover, a database was developed using PhpMyAdmin which consists of data regarding users and parking lots. This was done to solve the issue whereby no information was recorded regarding the availability of parking spaces at the IUKL campus. This paper can also serve as a template and guide for the complete implementation of a smart parking system in a university-based environment.

Desain Sistem Pendeteksi Plat Kendaraan Untuk Pengisian BBM Menggunakan Metode K-Nearest Neighbor (KNN)

This study aims to design and develop a vehicle plate detection system for refueling. Applications built using the K-Nearest Neighbor (KNN) method, this method is used to classify vehicle plates. This research will use the OpenCV module in the python programming language to recognize vehicle plates and carry out the pre-processing, segmentation, feature extraction, and classification stages of vehicle Image data. This system will utilize the K-Nearest Neighbor (KNN) method such as edge detection, perspective transformation, and pattern recognition to achieve optimal results. The test results show that the system is able to recognize with high accuracy various types of vehicle license plates under different lighting conditions and viewing angles, thus a system can be created and determined for each license plate to only be refueled once a day. When the vehicle has never been filled on that day, the system will display a green light which means refueling can be done. Whereas for vehicles that have filled in on that day, the system will display a red light which means the vehicle cannot fill up so it must wait the next day after the system is reset. This system will be able to accurately and real-time detect and identify the vehicle plates used in the refueling process. Therefore, the design of this system is expected to be the basis for increasing efficiency in the refueling industry as well as providing a basis for the development of further research in this field.

Detecting Vehicle Numbers Using Google Lens-Based ESP32CAM to Read Number Characters

plates continues to increase. This research aimed to detect vehicle license plates using ESP32CAM and utilize photo text reading using Google Lens, which can be used online to retrieve numeric characters. The method approach was to connect Wifi connectivity to the ESP32CAM, which had been programmed to detect vehicle plates. Vehicle plates that have been detected and recognized were inputted into Google Lens to capture the resulting text from the ESP32CAM camera recording. The results of this study were that for 70 seconds, ten plate samples were obtained, which were 100% perfect in reading license plates on Google Lens, namely six plates and two plates read 90%, one plate read 60%, and one plate read 0%. The research conclusions obtained were ten samples, six samples with perfect readings, and one error sample because of the white plate color. Thus, the main objective was to obtain the results of the vehicle plate detection and retrieve the text from the recording results

YOLO Based Advanced Smart Traffic Assistance Platform for Number Plate and Helmet Detection

Abstract: Now a days road accidents are one of the major causes that are leading to human death. However the most common reason for motorcycle deaths is because many fail to confirm to the law of wearing helmet. Here is the software using YOLO V8 to recognize the motorbike drivers , who are not obeying helmet law in an automated way. The helmet and license plate detection system using YOLO V8 is a computer vision technology-based system that utilizesthe You Only Look Once (YOLO) objectdetection algorithm to detect helmets and license plates in real-time. The system is designed to improve safety on roads and highways by detecting riders without helmets and vehicles without properlicense plates. The system consists of motorcycledetection , helmet and no helmet detection as well as bike license plate recognition. The system is capable of processing image from a variety of sources, including trafficcameras and drones, and can detect the presence or absence of helmets and license plates in the image frames. It uses a deep learning model trained on a large dataset of annotated images to identify and classify objects. The output of the system includes a bounding box around each detected object and a label indicating whether it is a helmet or a license plate. The system can also be configured to generate alerts or notifications when violations are detected. Overall, this system provides a valuable tool for law.