License Plate Detection Algorithm Research Articles

A lightweight license plate detection algorithm based on deep learning

AbstractLicense plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M.

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.

SLPR: A Deep Learning Based Chinese Ship License Plate Recognition Framework

Automatic ship license plate recognition (SLPR) for ship identification is of great significance to waterway shipping management. But few attention has been paid to SLPR in the past. In this paper, a novel cascaded Chinese SLPR framework consisting of the quadrangle-based ship license plate detection (QSLPD) algorithm and the rectification-based text recognition network (RTRNet) is developed. Concretely, in QSLPD algorithm, detection is performed based on the pyramid feature fusion architecture ameliorated by the proposed variable receptive field feature enhancement strategy and three task-specific output heads. In addition, a new loss function combining the dice coefficient and cross entropy is explored in the proposed SLPR which can generate significant improvement over the baseline. In RTRNet, regions of interest (RoIs) extraction and irregular text line rectification based on the vertices information predicted by QSLPD are performed before text recognition. Data augmentation are also applied to cope with the problem of limited text recognizer training data and the extremely imbalance distribution of corpus. Extensive experiments are carried out to demonstrate the reliability of the proposed cascaded SLPR framework, that can achieve the highest F-measure of 87.78% and 76.59% with IoU and TIoU metric on the collected dataset, surpasses many existing advanced methods.

License Plate Localization in Complex Environments Based on Improved GrabCut Algorithm

Aiming at the problem that the existed license plate detection method lacking of accuracy and speed, an improved lightweight detection algorithm for license plate detection in natural scenarios was proposed. First, the traditional GrabCut algorithm needs to interactively provide a candidate frame in order to perform the target detection work. We replace the candidate frame by introducing the Aspect ratio of the license plate as the foreground extraction feature to automate the detection of the license plate by GrabCut algorithm. Then, in order to improve the detection precision of traditional target detection algorithms, we introduced the Wiener filter, which is widely used in the field of digital signal processing, and Combine with Bernsen algorithm to complete image noise reduction. Finally, the algorithm was tested with the CCPD dataset, which contains many vehicle images from different complex natural scenes, especially low-resolution images. The experimental results shows that improved GrabCut algorithm achieves an average accuracy of 99.34% for license plate localization and a detection speed of 0.29s/frame, which has better accuracy and real-time performance compared with traditional GrabCut and other license plate localization algorithms.

Detection optimization of license plate targets based on AdvancedEAST

As an important core in the intelligent traffic management system, the technology and application of license plate recognition have become research focus. Detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of license plate recognition, which greatly affects the recognition rate and speed of the whole system. Nevertheless, due to the low accuracy of license plate detection in natural scenes, further investigations are still needed in this field in order to make the detection process very efficient. In this paper, We have studied and implemented a convolutional neural network license plate detection algorithm based on transfer learning. According to the invention, new energy license plates and ordinary license plates are adopted as the research objects. The text detection model AdvancedEAST is trained on the license plate images through the transfer learning method, and experiments are carried out on the self-built license plate dataset. The experimental results show that the algorithm can better adapt to light complexity, low resolution, target interference, license plate tilt and other complex conditions. The license plate positioning algorithm has high accuracy in natural scenes, and it is superior to the traditional license plate detection methods.

FAFNet: A False Alarm Filter Algorithm for License Plate Detection Based on Deep Neural Network

The license plate detection technology has been widely applied in our daily life, but it encounters many challenges when performing license plate detection tasks in special scenarios. In this paper, a license plate detection algorithm is proposed for the problem of license plate detection, and an efficient false alarm filter algorithm, namely the FAFNet (False-Alarm Filter Network) is proposed for solving the problem of false alarms in license plate location scenarios in China. At first, this paper adopted the YOLOv5 target detection algorithm to detect license plates, and used the FAFNet to re-identify the images to avoid false detection. FAFNet is a lightweight convolutional neural network (CNN) that can solve the false alarm problem of real-time license plate recognition on embedded devices, and its performance is good. Next, this paper proposed a model generalization method for the purpose of making the proposed FAFNet be applicable to the license plate false alarm scenarios in other countries without the need to re-train the model. Then, this paper built a large-scale false alarm filter dataset, all samples in the dataset came from the industries and contained a variety of complex real-life scenarios. At last, experiments were conducted and the results showed that, the proposed FAFNet can achieve high-accuracy false alarm filtering and can run in real-time on embedded devices.

Research on Chinese License Plate Recognition Algorithm Based on Convolution Neural Network

As an important part of intelligent traffic management, license plate recognition has very important research value. In order to solve the problem that the location of traditional license plate detection methods is not accurate and the detection results are easily affected by the environment, this paper proposes a license plate detection algorithm based on Faster R-CNN and Inception ResNet_v2, which realizes accurate license plate detection on VOC license plate data set by means of transfer learning. The effective extraction of a single character is realized by using the pixel statistics method in the detection results, and then the mLeNet5 convolution neural network model is designed to recognize the single character. The experimental results show that the algorithm can achieve efficient and accurate recognition of license plate characters with occlusion and angle tilt.

Smart Usage of Open Source License Plate Detection and Using IoT Tools for Private Garage and Parking Solutions

Open source license plate detection using internet of things is a modern day technology whereby the personal car activity entering the garage is identified by the cameras and the garage lights are lit up. When it leaves the garage lights are switched off. For an IP camera, the main image processing and search for number plates. The research here provides an important strategy for identifying, segmenting and understanding the characters within the located plate. We have proposed in this study a license plate detection algorithm and using internet of things simulation tool Node-red identifies the state transition for a car activity and light movements which are proposed in this work. The use of ‘Internet of things’ due to dynamic and self adapting nature of IoT to one server that facilitate in tracing a vehicle more easily over having manual database attached to each camera for identification purpose.

Vision-based nonintrusive context documentation for earthmoving productivity simulation

Abstract Although video surveillance systems have shown potential for analyzing jobsite contexts, the necessity of a complex multi-camera surveillance system or workers' privacy issues remain as substantive hurdles to adopt such systems in practice. To address such issues, this study presents a non-intrusive earthmoving productivity analysis method using imaging and simulation. The site access log of dump trucks is used to infer earthmoving contexts, which is produced by analyzing videos recorded at the entrance and the exit of a construction site. An algorithm for license plate detection and recognition in an uncontrolled environment is developed to automatically produce the site access log, by leveraging video deinterlacing, a deep convolutional network, and rule-based post-processing. The experimental results show the effectiveness of the proposed method for producing the site access log. Based on the site access log, simulation-based productivity analysis is conducted to produce a daily productivity report, which can provide the basis for earthmoving resource planning. It is expected that the resulting daily productivity report promotes data-driven decision-making for earthmoving resource allocation, thereby improving potential for saving cost and time for earthworks with an updated resource allocation plan.

A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN

The rapid development of the license plate recognition technology has made great progress for its widespread uses in intelligent transportation system (ITS). This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds. Firstly, a preprocessing is first used to strengthen the contrast ratio of original car image. Secondly, the candidate regions of license plate are checked to verify the true plate, and the license plate image is located accurately by the integral projection method. Finally, a new feature extraction model is designed using three sets of features combination, training the feature vectors through BPNN to complete accurate recognition of the license plate characters. The experimental results with different license plate demonstrate effectiveness and efficiency of the proposed algorithm under various complex backgrounds. Compared with three traditional methods, the recognition accuracy of proposed algorithm has increased to 97.7% and the consuming time has decreased to 46.1ms.

Fast Cartoon-Texture Decomposition Filtering Based License Plate Detection Method

Vehicle license plate detection is an important step in automatic license plate recognition, which is prone to be influenced by the background interference and complex environment conditions. It is known that cartoon-texture decomposition split an image into geometric cartoon and texture component, which can remove background interference away from the vehicle image. In this paper, we introduce a fast cartoon-texture decomposition filter into the detection process. Combining the edge detection, morphological filtering and Radon transform based tilt correction method, we formulate a new license plate detection algorithm. Experiment results confirm that the proposed algorithm can remove background interference away, inhibit the emergence of fake license plates, and improve the detection accuracy. Moreover, there is no inner loop iteration in the new algorithm, so it is fast and high-efficiency.

Automatic Traffic Surveillance Using Video Tracking

Road traffic and traffic congestion is major problem worldwide. This system uses video surveillance as it comes as the most economical technique for monitoring road traffic. Researchers have worked on different methodologies for video processing. The problems in existing methods are occlusions and variable lightening conditions. Also recent research on Indian roads proved that current image processing systems shows 55% median error on vehicle count. Proposed system looks into both day time as well as night time conditions to monitor traffic. Also it provides vehicle classification, traffic density, vehicle count, license plate detection and Incident detection. It combines many existing methods like background subtraction, kalman filter, 2-linesalgorithm,headlight detection, license plate detection algorithm The proposed system implements 2-lines algorithm and vehicle classification using kalman filter for day time and headlight based detection for night time which helps in successful tracking of vehicles. The license plate detection uses Edge detection, Gaussian Analysis, Feature extraction and character recognition which makes it robust to detect license plates in both day and night conditions.Median error was reduces to 11% by use of 2-lines algorithm. Vehicle classification using kalman filters gives accuracy of 82%.The proposed system will give median error less than 10% and accuracy of more than 90% in counting and classifying vehicles. The proposed system will be tested on MIT traffic data sets, MediaLab LPR database.

Multi-scaled license plate detection based on the label-moveable maximal MSER clique

In this paper, we consider a robust vehicle license plate detection problem for intelligent transportation systems in the presence of various illumination situations. We propose a robust and fast multi-scaled license plate detection and location algorithm, which exploits a Label-Moveable Maximal MSER clique. Specifically, first, we extract the candidate character regions using the Maximally Stable Extremal Region (MSER) features. Second, we divide each candidate character region into four types and extract the suspected initial node (the top-left character) based on its neighbor MSER distribution characteristic. Third, we label each candidate character region to accomplish license detection and location based on the detected suspected initial node and the corresponding label-moveable maximal MSER clique. The robust of license plate detection, the accuracy of character labeling for license location, and the improvement of calculation efficiency are evaluated via the real data.

Component-Based License Plate Detection Using Conditional Random Field Model

This paper presents a novel algorithm for license plate detection in complex scenes, particularly for the all-day traffic surveillance environment. Unlike low-level feature-based methods, our work is motivated by component-based models for object detection. The detection process is divided into three steps, namely, decomposition, modeling, and inference. First, observing that one license plate is decomposed into several constituent characters, the maximally stable extremal region detector is used to extract candidate characters in images. Then, conditional random field (CRF) models are constructed on the candidate characters in neighborhoods. This way, the spatial and visual relationships among the characters is integrated in CRF in the form of probability distribution. Finally, the exact bounding boxes of license plates are estimated through the belief propagation inference on CRF. Both visual and structural features of license plates are fully exploited during detection. Hence, our approach can adapt to various environmental factors, such as cluttered background and illumination variation. A series of experiments are conducted on images that are collected from the actual road surveillance environment. The experimental results show the outstanding detection performance of the proposed method comparing with traditional algorithms.

PPCA-based license plate detection algorithm

Detection of license plates is a critical part of License Plate Recognition (LPR) systems. In this paper we employ methods based on Probabilistic Principal Component Analysis (PPCA) to extract license plate areas out of vehicle images, and apply an im- proved normalization step to data preprocessing. The analysis of our experiment shows that this algorithm achieves good accuracy dealing with still vehicle images.