Research on Car License Plate Recognition Based on Improved YOLOv5m and LPRNet
نویسندگان
چکیده
The application of license plate recognition technology is becoming more and extensive. In view the current practical requirements for accuracy real-time performance system in complex scenes, existing target detection methods are studied, a car method based on improved YOLOv5m LPRNet model proposed. On basis studying algorithm image features plate, from three aspects: K-means++ used to improve matching degree between anchor frame target, DIOU loss function NMS method, feature map with 20×20 removed reduce number layers. A lightweight network realize character without segmentation. Combining network, IYOLOv5m-LPRNet designed. experimental results show that average plates front, tilt, night strong light interference scenes than 98%; Compared models YOLOv3-LPRNet, YOLOv4-LPRNet, YOLOv5s-LPRNet YOLOv5m-LPRNet, recall rate this improved, reaching 99.49% 98.79% respectively; mAP also highest, 98.56%; terms speed, faster other four methods, pictures processed per second increased by 5 compared YOLOv5m-LPRNet model. Therefore, paper performs well robustness speed.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3203388