Research On License Plate Recognition Technology Based On Deep Learning In Complex Environment

In recent years,the development of economy and science and technology has embarked on a"fast track",and people's requirements for living standards have also become higher and higher.Smart cities are no longer just a concept of nothingness,but gradually begin to serve people's daily lives.As an important branch of the smart city,intelligent transportation has aroused the interest of researchers.In natural scenes,the use of license plate recognition technology can quickly,efficiently,accurately and orderly manage intelligent transportation.However,for traditional license plate recognition systems,the algorithm has low robustness and insufficient anti-interference ability.In complex scenes such as poor lighting conditions,tilted license plates,and dirty damage,the effect of license plate positioning and recognition will be greatly reduced.For these problems,this paper has conducted targeted research,aimed at proposing a license plate recognition technology that can be applied to complex scenes.Through the analysis and research on the previous license plate recognition technology,it's concluded that the traditional license plate recognition algorithm is composed of three steps:license plate positioning,character segmentation,and license plate recognition.Considering the actual scenario of applying license plate recognition technology,this paper summarizes the more widely used algorithms in these three steps,analyzes and discusses,and points out the advantages and disadvantages of each algorithm.In order to prevent the accumulation of errors due to "divide and conquer",this paper proposes a license plate recognition technology based on YOLO-tiny,and applies the idea of end-to-end network,which effectively avoids the accumulation of errors in traditional license plate recognition systems.In this paper,a large number of license plate samples in complex scenes are collected.Using machine-assisted and manual annotation methods,training sets and test sets in different scenarios are produced.Through these image data sets,the license plate recognition technology studied in this paper is trained and tested.The final test results show that,compared with the traditional license plate recognition technology,the license plate recognition technology proposed in this paper performs better both in terms of detection speed and detection accuracy.