The Application of Image Processing Technology in the Intelligent Transportation System

Automatic License Plate Recognition (ALPR) has been the focus of many\nresearches in the past years. In general, ALPR is divided into the following\nproblems: detection of on-track vehicles, license plates detection, segmention\nof license plate characters and optical character recognition (OCR). Even\nthough commercial solutions are available for controlled acquisition\nconditions, e.g., the entrance of a parking lot, ALPR is still an open problem\nwhen dealing with data acquired from uncontrolled environments, such as roads\nand highways when relying only on imaging sensors. Due to the multiple\norientations and scales of the license plates captured by the camera, a very\nchallenging task of the ALPR is the License Plate Character Segmentation (LPCS)\nstep, which effectiveness is required to be (near) optimal to achieve a high\nrecognition rate by the OCR. To tackle the LPCS problem, this work proposes a\nnovel benchmark composed of a dataset designed to focus specifically on the\ncharacter segmentation step of the ALPR within an evaluation protocol.\nFurthermore, we propose the Jaccard-Centroid coefficient, a new evaluation\nmeasure more suitable than the Jaccard coefficient regarding the location of\nthe bounding box within the ground-truth annotation. The dataset is composed of\n2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and\ntheir corresponding bounding box annotations. We also present a new\nstraightforward approach to perform LPCS efficiently. Finally, we provide an\nexperimental evaluation for the dataset based on four LPCS approaches and\ndemonstrate the importance of character segmentation for achieving an accurate\nOCR.\n

The intent of this research is to design a license plate recognition (LPR) system in the domain of Bangla language for smart vehicle management. The proposed system is designed on the basis of computer vision tools and deep supervised machine learning model. The system has three modules: license plate detection, character segmentation and recognition of the characters of the License Plate (LP). The goal of detection is to localize the plate area from the vehicle image and to crop region of interest (LP). It is executed by applying following process: preprocessing the image, conversion to binary image, contour detection and filtering the contours to get the LP's character contours, tilt correction and cropping the plate area from the image. Then, the cropped LP is segmented to extract the characters from the plate. Finally, the recognition step classifies the characters by means of deep convolution neural network where the features of the character are crafted and learned by the convolution layers of the networks. The system is implemented in Python OpenCV environment for offline car license plates images which are taken in different illuminations, road scenarios and colored cars. The system performance is evaluated in terms of detection rate, segmentation rate, recognition rate and execution time. The results illustrate that the performance of the system is remarkable.

With the increasing advancement of science and technology, the popularity of automobiles in my country has gradually increased. Among them, license plate positioning and license plate recognition technology are very important key technologies in the license plate recognition system. Through the license plate positioning technology, the position information, centroid and other data information of the license plate can be obtained, so as to realize the positioning of the license plate. The license plate recognition technology can extract the uniqueness of the vehicle. Identifying the license plate number, so the design and implementation of vehicle recognition and license plate recognition technology based on embedded devices have practical significance and practical application value to promote the technological progress of intelligent transportation and vehicle recognition systems. This design is a license plate recognition system, an embedded character segmentation recognition processing platform with ARM Cortex-A9 as the core, and the method of cross-compiling OpenCV, Qt, BootLoader in the Linux operating system, and transplanting related drivers to the ARM Cortex-A9 platform; Based on the classic algorithm, the OpenCV library function is used to realize the segmentation and recognition of the license plate image characters. The experimental results show that the design has achieved the expected goal.

In this paper, we propose a novel image segmentation algorithm for license plate recognition (LPR) in video based traffic surveillance system. The license plate character segmentation is most important procedure in LPR system. However, in real situation, the character segmentation algorithms are challenged by drastic performance decrease due to sudden local illumination changes, especially when the color of characters is similar to that of background in LP. To mitigate this problem, we introduce a novel LP character segmentation algorithm by employing an adaptive binarization method using super-pixel based degeneracy factor. The proposed method demonstrates a significant improvement over conventional methods.

On the streets of Taiwan, many roadside tollers are often seen riding motorcycles in one hand, and the other hand holding mobile devices to issue payment notices for cars and motorcycles parked on the roadsides. The work of roadside tollers is very dangerous. First, they must first park their motorcycles next to the roadside cars and motorcycles. They then use their eyes to confirm the license plate number, enter the license plate number into the mobile device, and finally place the bill on the car’s windows or attach the bill to the motorcycles. Our idea is to implement an automated license plate recognition system in mobile devices to increase the efficiency of roadside tollers and reduce their time on the road. Recently, license plate recognition systems have been widely used in various aspects of life, such as parking lot toll systems, access management systems, and traffic management systems. However, existing license plate recognition systems must have good recognition rates under a number of constraints, such as fixed angles and fixed light sources. Moreover, due to the insufficient computing resources of the general mobile device, the application cannot have a good recognition rate in the complex environment or skewed angle in the license plate recognition. Therefore, this paper proposes a lightweight and high-performance multi-angle license plate character recognition model, which reduces the complexity and computational complexity of traditional license plate recognition. This paper also collects a large number of license plate images from different environments, angles and sizes as training data. Finally, we propose an optimized deep learning model to identify the characters on license plates. The experimental results show that the proposed model can recognize the license plate with a tilt of 0~60 degrees, and the overall recall rate is 84.5%. Compared with Tiny-YOLOv2, the computation of the proposed model is reduced by 61% with a little penalty of recall.

In this paper, we present a robust Chinese license plate recognition system. The system consists of three modules: license plate localization, character segmentation and character recognition. In the license plate localization module, Color point pairs are searched to produce the plate region candidates coarsely, and the shape, texture features are used to refine the candidate. In character segmentation module, skew Correction, Character Segmentation and Thinning Processing is employed to improve the segmentation performance. In the character recognition module, SVM classifiers are used to generate effective result. Experimental result shows that License plate localization achieved accuracy of 94.19% and character recognition 94.51%.

The license plate recognition technology is an important part of the construction of an intelligent traffic management system. This paper mainly researches the image preprocessing, license plate location, and character segmentation in the license plate recognition system. In the preprocessing part of the image, the edge detection method based on convolutional neural network (CNN) is used for edge detection. In the design of the license plate location, this paper proposes a location method based on a combination of mathematical morphology and statistical jump points. First, the license plate area is initially located using mathematical morphology-related operations and then the location of the license plate is accurately located using statistical jump points. Finally, the plate with tilt is corrected. In the process of character segmentation, the border and delimiter are first removed, then the character vertical projection method and the character boundary are used to segment the character for actually using cases.

The rapid development of urban intelligence has turned intelligent transport system (ITS) development into a primary goal of traffic management. Automated license plate recognition (ALPR) for moving vehicles is a core aspect of ITS. Most ALPR systems send images back to a server for license plate recognition. To reduce delays and bandwidth use during image transmission, this study proposes an edge-AI-based real-time ALPR (ER-ALPR) system, in which an AGX XAVIER embedded system is embedded on the edge of a camera to achieve real-time image input to an AGX edge device and to enable real-time automatic license plate character recognition. To assess license plate characters and styles in a realistic setting, the proposed ER-ALPR system applies the following approaches: (1) image preprocessing; (2) You Only Look Once v4-Tiny (YOLOv4-Tiny) for license plate frame detection; (3) virtual judgment line for determining whether a license plate frame has passed; (4) the proposed modified YOLOv4 (M-YOLOv4) for license plate character recognition; and (5) a logic auxiliary judgment system for improving license plate recognition accuracy. We tested the proposed ER-ALPR system in selected real-life test environments in Taiwan. In experiments, the proposed ER-ALPR system achieved license plate character recognition rates of 97% and 95% in the day and at night, respectively. Through the AGX system, the proposed ER-ALPR system achieves a high recognition rate at a low computational cost.

License plate character segmentation is an important step of license plate recognition (LPR). This paper proposes an approach to segment license plate character based on stroke width transform (SWT). The method detects some character candidates by SWT, reserves real character regions with cluster analysis based on horizontal position and the height of detected candidate regions, and fits two straight lines. According to the tilting degrees and locations of fitted lines, locate precisely position of a plate, and correct the rotation plate. At last, segment characters using connected component analysis and template matching. Experiments show that the proposed method can accurately segment license plate characters, has good adaptability to rotation, slightly fouling. It also saves to be difficult to adjust parameters with a general method to deal with different scale plates, meanwhile not necessary to preciously locate the position of plates before segmentation.

Automatic Identification of vehicles is a very challenging area, which is in contrast to the traditional practice of monitoring the vehicles manually. Automatic license plate (LP) recognition is one of the most promising aspects of applying computer vision techniques towards intelligent transportation system. In Location of the vehicle plate, a method of vehicle license plate character segmentation and extraction based on improved edge detection and Mathematical morphology was presented. In the first place, color images were changed into grey images, secondly through calculates the difference of each pixel and neighbourhood pixels to build up images edge and it can make the license plate stand out; Sobel operator is used to extract the edge of objects in image; then the algorithm applies the dilation and erosion mathematical morphology of binary images to get the image smooth contour. The segmentation result which is sent forward to LP recognition stage will improve further processing’s efficiency . Neural Network is used to recognize the license plate character. Because of the accuracy of the plate region extraction, the character can be extracted exactly from the plate region .

License plate recognition (LPR) technology is a currently more popular pattern recognition processing technology, which has broad application prospects. In this chapter, based on the original LPR technology, a novel method of LPR based on the Sobel operator is presented. We improve the accuracy of LPR by establishing an eight-direction template, and the test results show that our improved algorithm is effective. Then we use the k-means algorithm for character segmentation of license plate to get the better license plate image segmentation.

Automatic License plate detection and recognition system is special form of optical character recognition and has been an active research domain in image processing field. However, the accuracy is varied due to different styles of number plates endorsed. Besides, the characters on Malaysia license plate are in one or two lines. Thus, the proposed license plate recognition (LPR) technique of this research is able to achieve the best recognition performance based on Malaysia license plate vehicle registration number characters. This paper presents a study of applying the template matching method for character image recognition. The database of characters and license plate image has been created by collecting images from various type of car. The initial pre-processing involves image enhancement, binarization, filtering and segmenting of license plate. There are 100 license plates that contain 693 characters have been tested, and the result shown that 92.78 % of all characters is correctly recognized. Thus, template matching can be classified as one of the promising algorithm for recognizing Malaysia license plate.

As the difference of gray value between the numbers and letters on the license plate and the license plate background is very big. We first use region growing algorithm based on the gray value to separate each number and letter into different area. Secondly, we search for the bounding rectangles of all regions and find some rectangular sequences that meet a special condition. Thirdly, calculate the horizon bias for all rectangles according to the coordination of the up-right point in the rectangle, adjust and get the precise location of each rectangle. Forth, we get the binary image which can separate the numbers and letters on the license plates and the license plate background by the dynamic threshold algorithm. The relation of the maximum width of various license plate numbers and letters and its bounding rectangles helps to count the direction of the vertical bias, then the bias is adjusted. At last we statistic the foreground vertical histogram of the previous results, and get the result of precise vertical position and character segmentation. The experimental results show that this algorithm performs well for location of multi-national license plates.

The city's efforts to improve traffic make a big step forward when license plates can be read. It explains how an intelligent transportation system should work and what steps should be taken for it to be successful. Because the number of cars is growing so quickly, ANPR is a must-have for managing traffic control. Automatic number plate recognition (ANPR) is mostly used to keep an eye on traffic and keep people safe. Number plate recognition uses image processing and the most recent advances in technology to automatically read the characters on a vehicle's license plate. In the last few years, there have been a number of technological advances in the field of studying how to read license plates. Image processing techniques like OCR make it possible for traffic surveillance to solve a wide range of problems that come up during criminal investigations, the collection of tolls, the monitoring of traffic, the regulation of speed, and the management of parking, among other things. If you want to control traffic and keep an eye on a large number of people in a transportation system, you need an ANPR system. Image processing techniques and the collection of photos of vehicles for use in the dataset have made it possible to keep an eye on traffic on a large scale. Automatic License Plate Reader (ANPR) is a way to take the steps needed to run a good intelligent transportation network. Traffic control has become an absolute must because the number of cars has grown so quickly. The main goal of ANPR is to keep an eye on and record traffic for defense purposes. To read the text on license plates, number plate recognition uses image processing, optical character recognition (OCR), and edge detection technology. The model is made up of three different parts called, respectively, the module for car detection, the module for license plate segmentation, and the module for recognition. Image processing made us more determined to stop a wide range of illegal activities, such as armed robberies of cars, breaking traffic rules, and the way law enforcement is handled. This review study looked at all of the different designs for reading license plates that have been put into use so far.

CIS Multilingual License Plate Detection and Recognition Based on Convolutional and Transformer Neural Networks