In order to solve the problem of low accuracy and poor generalization of license plate detection,this paper proposes an algorithm based on improved Yolov3 vehicle recognition and Mask R-CNN license plate detection.By detecting the vehicle and license plate separately in two steps,a YOL3-RCNN multi license plate recognition mod-el was ultimately constructed.The model first integrates the large,medium and small features of the image based on the up sampling algorithm,and uses the morphological clustering algorithm to adaptively calculate the IOU threshold to improve the vehicle detection rate and accuracy;Next,the detected vehicles are cropped,optimized with SOFTNMS and affine transformation to solve the problems of license plate occlusion,tilting,and complex background;Finally,the characters and digits of the license plate are recognized based on ResNet50 network,and the regression verification is carried out by using RPN.The simulation results show that the accuracy and recall of YOL3-RCNN al-gorithm are improved by 7.7% and 9.6%,respectively,compared with the traditional license plate detection algorithm on the UA-DETRAC vehicle detection dataset,and it has a higher license plate detection rate.To sum up,the YOL3-RCNN multiple license plate recognition model constructed in this paper has high detection accuracy,timeliness and universality.
关键词
复杂道路环境/大流量路口/多车牌自动识别
Key words
Complex road environment/High traffic Intersections/Automatic recognition of multiple license plates
维普
万方数据