Abstract
Automatic Number Plate Recognition (ANPR) systems were created in order
to address the challenges created due to rise in the volume, velocity
and density of automobile vehicles. Most modern ANPR systems heavily
depend on image processing techniques like contouring and gray-scaling
to segment the license plate and use optical character recognition to
extract the number from the manipulated image of the number plate. The
major advantage of such systems is that they require less computational
power and are quite cost-effective. But these systems process the
complete images rather than just the region of interest. The more modern
ANPR systems rely on object detection algorithms to overcome this
challenge and process only the area of interest from the image. But
these systems are not suitable for real-time applications due to high
computational overhead and high processing time. To overcome the
limitations of the existing ANPR systems, we propose an intelligent ANPR
is a system for detecting and extracting number plate details from
images or sequences of images of vehicles. The proposed deep learning
model is based on an improved version of the You Only Look Once (YOLOv4)
algorithm. The performance evaluation demonstrates that our model is
able to recognize the number plates with an accuracy of above 95% under
varied conditions such as high-speed moving vehicles, varying lighting,
and vehicle dense area on Indian roads. The system is robust enough to
detect the number plates of fast moving vehicles (speed≤ 80km/hr) as
well as vehicles in highly traffic dense areas.