Abstract:
In recent years computer vision models have made our daily life easy in various ways,
especially in reducing roadside problems. Many research works are already completed
to achieve the goal of automated road surveillance. But these models' actual
implementation has failed due to the poor accuracy of the model and other relevant
factors. This paper presents an improved model to detect, extract, recognize and validate
Bengali license plates from vehicles. In order to recognize vehicle plates more accurately
and for various uses, including automated vehicle monitoring, roadside assistance, toll
collection, parking management, etc., we implemented a Yolo-based CNN model to
detect Bangla license plates and mask R-CNN for recognition of license characters. A
total of 6528 images were used in training our model. Based on roadside test images, the
experiments can detect at a rate of 98.2%, recognition of 95.6%, and a validation rate of
100%, respectively.
