Detecting Vehicle Type and License Plate Number of different Vehicles on Images
This addresses urban traffic management challenges by enabling specific vehicle identification, but it is incremental as it combines existing methods.
The paper tackles vehicle tracking by developing a model that detects vehicle type and license plate number from images, using Mask R-CNN for type detection and WpodNet with pytesseract for license plate recognition.
With ever increasing number of vehicles, vehicular tracking is one of the major challenges faced by urban areas. In this paper we try to develop a model that can locate a particular vehicle that the user is looking for depending on two factors 1. the Type of vehicle and the 2. License plate number of the car. The proposed system uses a unique mixture consisting of Mask R-CNN model for vehicle type detection, WpodNet and pytesseract for License Plate detection and Prediction of letters in it.