Vehicle license plate detection and recognition in complex road environments
A gray binarization image preprocessing method based on a global threshold is proposed to aim at the issues of poor positioning detection effect and low recognition accuracy caused by tilt,blurring and occlusion of license plates in complex road.YoloV51 algorithm is adopted to conduct positioning detection and evaluate detection results on data sets in the post-processing stage.R-CNN model is used to recognize the license plate image characters after location detection.The results show that when the training process continues to 100 rounds,compared with the Faster R-CNN algorithm,the mean average precision(mAP)of the model detection is improved by 9.2％,and the recognition accuracy is improved by 17.33％,which verifies the effectiveness and superiority of this method in detecting and recognizing license plates.
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