Research On Foggy License Plate Recognition Technology Based On Image Processing

In recent years,severe weather such as smog has occurred frequently in my country.In the haze weather,due to the effect of atmospheric light scattering,the license plate image obtained by the image acquisition device is seriously degraded,that is,the surface of the license plate image is covered with a layer of fog,and the image is blurred and the color information is largely missing.These factors have a serious impact.The effectiveness and reliability of the intelligent transportation system.Therefore,the subject has carried out research on the license plate recognition technology under fog and haze weather to solve the problem of low license plate recognition rate under fog conditions,thereby improving the functional integrity and reliability of the intelligent transportation system.In haze weather,there are many factors that affect the efficiency and accuracy of license plate recognition.These factors are mainly the persistence of fog in the image and the resolution of the license plate image.Therefore,this article conducts in-depth research from the following aspects:(1)Fog-removing algorithm for license plate images in fogThe non-local TGV regularization method is used to correct the initial transmittance of the image,and the second-order non-local regularizer is used as a regularization term to ensure robustness to outliers caused by noise and ambiguity between image color and depth Then,the initial transmittance of the corrected and refined image is brought into the side window box filter to obtain an accurate transmission map,which solves the disturbance caused by the intermediate processing step.Finally,the fog-free license plate image is recovered using the atmospheric scattering model and constraints.Through two evaluation experiments,the dehazing effect of the proposed algorithm is explained.(2)Super-resolution reconstruction algorithm for license plate imageAnalyze information such as the resolution and color of the foggy image.On the Cr and Cb channel map of the license plate,the up-sampled channel image is interpolated using the interpolation method based on the Gaussian kernel function to fill in the missing high-resolution pixels.On the Y-channel map of the license plate,the low-rank attribute of the image is used to restore the license plate,and the reconstruction effect is compared with each algorithm.(3)Research on license plate location algorithmThrough the analysis of edge detection and mathematical morphology,the improved edge detection algorithm and mathematical morphology operation are combined to realize the location of foggy license plates.Aiming at the problem of slanted license plate and redundant frame,the Hough transform algorithm is used to correct the slanted license plate.By counting the number of jumps and the area scanning pixel value,the top and bottom and left and right borders of the license plate are added and removed.(4)License plate segmentation and character recognitionBy designing templates corresponding to various license plates,segmentation of various types of license plate characters is realized.Using the improved Le Net-5 license plate character recognition network model,setting the parameters in the Le Net-5 network model by modifying the number of layers,activation function and training type of the convolution,the parameters of the Le Net-5 network model are compared with the recognition accuracy and time-consuming aspects.SVM algorithm and the classic Le Net-5 network model algorithm are compared and tested.(5)Development of foggy license plate recognition system softwareAccording to the image processing algorithm proposed in this paper,the foggy license plate recognition system software is designed.It mainly includes two parts: single recognition operation and rapid recognition operation,which can realize the rapid recognition of foggy license plates and obtain the algorithm processing effect of the intermediate steps.Finally,use the designed foggy license plate recognition system software to perform a functional test on the foggy license plate image taken outdoors to test its recognition effect and accuracy.