Affordable AI - Named after muhammad al-khorezmi tashkent university of information technologies fergana branch

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Named after muhammad al-khorezmi tashkent university of information technologies fergana branchBog'liq m121. Affordable AI: Visibility and clarity are becoming increasingly important when AI is included in ANPR technology. Future research might concentrate on developing artificial intelligence algorithms that could clarify what they choose, make them simpler to learn, and tackle problems with mistakes or unfairness.
2. Flexible Techniques: The correctness of ANPR might be improved by integrating AI with new innovations like LiDAR technology, radar detectors, and infrared detectors, specifically in difficult circumstances involving the environment.
Pic 1.2.3
3. Edge Computing: Edge technology includes decreasing congestion and enhancing real-time features by handling information located near its original location. AI use at the point of detection may allow for quicker and more successful ANPR analysis.
4. Continuous Development: ANPR technologies can be programmed to be flexible enough to modify situations and transform license plate patterns as time passes by using ongoing instruction technologies. As a result, fewer regular modifications to the models could be required.
5. Regulation Structures: The safety and security concerns raised by AI-powered ANPR technology will probably be addressed by law enforcement and government agencies through the establishment of protocols. Following simple rules helps guarantee proper implementation and operation.
P ic 1.2 4 Typical ANPR System Diagram
The development of license plate recognition technologies has significantly advanced with the incorporation of AI into ANPR technologies. Since AI can improve reliability, flexibility, and immediate analysis, ANPR technology varies from simple license plate monitors to advanced devices with a variety of functions. Although there are issues with privacy, partiality, and needed resources that should be solved, there are undisputed advantages when it comes to public security, traffic control, and safety. The ANPR system will probably improve in effectiveness, accuracy, and moral responsibility as AI develops, helping to shape the direction of transportation and technology for monitoring.
Worldwide license plate recognition research and development
License plate recognition systems are widely used in modern smart cities, such as toll payment systems, parking fee payment systems and residential access control. Such electronic systems are not only convenient for people’s daily life, but also provide safe and efficient services for managers. License plate recognition algorithm is a mature but imperfect technology. The traditional location recognition algorithm is easily affected by light, shadow, background complexity or other factors, resulting in the failure to meet the application of real scenes. With the development of deep learning, the license plate recognition algorithm can extract deeper features, thus greatly improving the detection and recognition accuracy. Therefore, this paper discusses the application of deep learning in license plate recognition, and the main work is as follows: 1) Introduce the most advanced algorithms from the three main technical difficulties: license plate skew, image noise and license plate blur; 2) According to the process, the deep learning algorithms are classified into direct detection algorithms and indirect detection algorithms, and the advantages and disadvantages of the current license plate detection algorithms and character recognition algorithms are analyzed; 3) The differences in data sets, workstation, accuracy and time of different license plate recognition systems are compared; 4) Compare and illustrate the existing public license plate datasets according to the number of pictures, resolution and environmental complexity, and make a prospect for the future research direction of license plate recognition.
With the rapid development of economy around the world, some cities in different countries may faced with traffic congestion, frequent accidents, traffic environment deteriorating or other urban traffic problems. Along with the increase in car use, the following problems in the process of car also gradually emerge, such as: car theft case [1], traffic accident, road congestion, serious environmental pollution, and so on. In order to solve these problems, each country is actively studying how to manage and monitor vehicles more effectively. If only rely on human resources such as traffic police, it will bring many problems such as high cost and low efficiency. Therefore, if intelligent traffic equipment can be introduced, it will undoubtedly bring great convenience and advantages. In this context, the research on Intelligent Transport System (ITS) [2] was born. The concept of ‘‘smart city’’ arises as the times require, ‘‘intelligent video surveillance’’ and ‘‘intelligent transportation’’ are gradually put on the research agenda to realize intelligent management of cities. License plate recognition system has been widely used in vehicle access management, expressway toll management, intelligent parking, electronic police and other aspects, which plays an important role in the supervision of vehicles, and can realize the supervision of urban traffic to prevent traffic jams, has important significance in real life. At present, there are many license plate recognition systems, but in the complex environment (such as lighting conditions, distorted license plate, dirt license plate, etc.), their license plate recognition rates will be greatly reduced. So how to improve the license plate detection accuracy and recognition rate in the complex environment [3] has great research significance.
Pic 1.3.1 Process of license plate recognition system.
The license plate recognition system has two main tasks, one is to locate the license plate, and the other is to identify the license plate characters. The process is shown in Pic 1, which is generally divided into three steps, namely license plate location, character segmentation and character recognition. Recently, some people have proposed a two-stage algorithm combining with sequence recognition, therefore the recognition process can only be divided into license plate location and character recognition, leaving out character segmentation. Character segmentation [4] is often used in traditional text recognition algorithms, which use prior knowledge such as fixed character spacing, connected component analysis and project-based methods to implement segmentation. However, due to the handcraft features often can not be used to accurately segment, so the segmentation-free algorithm like sequence labeling can effectively avoid the character segmentation error affecting the recognition accuracy. For noisy scenes, some researches denoise the image and improve the resolution before the license plate detection. For some scenes with skewed shooting angles, some researches have proposed to use the tilt correction algorithms to correct the license plates or the segmented characters to improve the recognition rate.
With the application of license plate recognition technology has become more and more widely, people enjoy the convenience of license plate recognition technology, but can not avoid many difficulties in the process of license plate recognition. The research content of license plate recognition has changed from conventional pictures to complex environments. The main challenges are divided into three aspects:
1) License plate deflection: Most of the datasets used in the early license plate recognition studies are similar to Caltech Car [5] and English LP [6]. These datasets are relatively simple, for example, most images were captured with a handheld camera and there is only one vehicle (generally wellcentered) in each image. However, recent datasets, such as Chinese LP [7] and UFPR-ALPR [8], include multiple cars in each image, with the unfixed shooting angle and distance, and the license plate has different degrees of deflection.
2) Noisy plate images: In the real scene, rain, snow and other weather will inevitably occur. In these conditions, some license plates will be blurred and blocked by rain or snow, while some license plates have uneven lighting. The background is no longer a simple parking lot without other vehicles, but a place with complicated lines just like street intersections with people coming and going
3) Fuzzy license plates: Freeway monitoring is one of the major applications of license plate recognition system, which often capture the image containing fast moving vehicles. And these vehicles images are usually small object images, for example, in 1597∗1197 size vehicles images, the boxes size of vehicle are usually around 533∗522, nearly 14% of the whole image and the boxes of the license plates are usually around 123∗36, nearly 0.2% of the whole image. Additionally, with the high-speed movement of vehicles the license plate characters may become fuzzy. Therefore, there are many researches about how to effectively improve the image resolution of the designated area.
Automatic Number Plate Recognition has become part of our lives and promises to stay in future, integrable with proposed transportation technologies. The concept of Autonomous Vehicles is offering many possibilities of changing fundamental transportation systems. ANPR technology is already contributing towards intelligent transportation systems and is eliminating the need of human intervention. It is no longer just the camera on the roadside or at the barrier to the car park. It has become over the years mobile, first being deployed in vehicles, but now more recently with the advent of smart phone technology, many ANPR systems have become handheld too. Due to lower provisioning costs, ANPR is often a choice in the toll and parking lot businesses. The main reason is that the ANPR system recognizes the registered number plate with no additional transponder requirements, as compared to the Ultra High Frequency—Radio Frequency Identification (UHF-RFID) systems. The rapid urbanization of countries is a great advancement in our modern world. People migrate away from rural areas and choose to live in cities mostly. Local governments often fail to recognize the present and potential mobility needs of residents and visitors as traffic rises in these areas. ANPR is being increasingly used to examine the free flow of traffic, facilitating the intelligent transportation [1].
Not only can modern ANPR cameras read plates, but they can provide useful additional information such as counting, direction, groups of vehicles and their speed. The ability to detect and read large volumes of fast moving vehicles has meant that ANPR technology has found its way into many aspects of today’s digital landscape. Whilst ANPR technology can come in many different packages, they all perform the same basic function which is to provide a highly accurate system of reading a vehicle without human intervention. It is utilized in very diverse applications such as access control, parking management, tolling, user billing, delivery tracking, traffic management, policing and security services, customer services and directions, the red light and lane enforcement, queue length estimation, and many other services. Pic 1.3.1 shows the basic system diagram of a fixed and mobile ANPR technology.
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