Detection and Recognition of Bangladeshi Vehicles' Nameplates Using YOLOV6 and BLPNET

Abstract

An effective license plate identification algorithm reduces administration expenses while simultaneously enhancing traffic management effectiveness. The novel method suggested in this paper is based on the YOLOv6 amplified convolution model and has two components: Nameplate recognition and location. As a result, the model's receptive field and feature expression capability are improved. For license plate location, CIOU loss takes into account the center distance, aspect ratio, and not just the coverage area of the bounding box. According to the studies, the YOLOv6 model has a 94.7% precision rate for locating license plates, which is 5.6%, 5.1%, and 4.3% better than Faster-RCNN, MobileNet, and the corresponding accuracy rates. We proposed a BLPNET(VGG-19-RESNET-50) model to recognize the characters of number plates and achieved a 100% F1 score.