Research On Target Detection And Recognition Algorithm In Open Environment

License plate recognition technology for traffic management systems around the importance of self-evident,it has attracted the attention of many researchers in recent years.However,in the absence of light or even in the dark,the images collected by most existing ordinary image acquisition equipment have problems such as low quality,insufficient clarity,and insufficient brightness.On the other hand,the issue of night traffic monitoring and license plate recognition in low-light environment is becoming more and more important in today’s society,which is a research topic with practical significance.In order to solve the above-mentioned problems in the research of license plate recognition under low light environment,this paper proposes an effective license plate detection and automatic recognition solution combining image restoration and neural network method after referring to a large number of literatures and conducting many experiments.When the low-light video is inverted,it has a high similarity to the video captured in a hazy light environment such as a foggy environment.Analogously to the inverted low-light image,we invert the input original low-light image and then use atmospheric light.The model uses the defogging method combined with the filtering operation to restore a high-quality image.Finally,a clear image with the proper illumination is required.Because the method of mean filtering avoids the complex calculation of estimating the atmospheric light,which provides higher quality sample images for subsequent processing.In order to solve the problem that the edge detection effect is not ideal in the low illumination environment,we use the HSI color model combined with the color positioning to further improve the success rate of the target area positioning.For the pre-processed images,gaussian filtering is first used to eliminate the noise before edge detection and color features are combined.The candidate regions are preliminarily determined by using the texture structure features of the target region,and other non-target regions are excluded by prior knowledge and SVM model to further accurately locate the license plate.The character segmentation method is adopted in combination with prior knowledge for the special structure of Chinese characters(stroke and relative position),and a character segmentation method is obtained which is more suitable for the special structure of Chinese characters.In the end,as a result of the traditional Le Net-5 network identification object is handwritten Numbers,unable to meet the license of the Chinese and English character recognition task,so this article on the traditional Le Net-5 model has made the corresponding improvement,and the three data sets collected under various environment image on a sample of the performance test,the results show that the license plate detection and automatic identification solutions in an open environment especially under the condition of extreme light can achieve higher recognition accuracy,better robustness.