A One-stage Modified Tiny-YOLOv3 Method for Real-time Moroccan License Plate Recognition

Abstract—Automatic license plate recognition systems (ALPRS)
have a major significant role in supporting smart city
initiatives because of their promising applications in various
areas, such as road safety, traffic control, smart parking, toll
management, and security. Owing to ALPRS’s importance, numerous
methods have been developed. However, many of the
present solutions are still suffering from some inefficiencies in
real-world situations, generally related to many constraints. In
this work, we address the problem of Moroccan car license
plate detection and recognition based on the state-of-the-art
YOLO deep learning framework. To this end, we have made
improvements in the Tiny-YOLOv3’s network structure to make
it more rigorous. We proposed a real-time end-to-end ALPRS
using a one-stage approach. The network is trained using images
from a collected large-scale dataset of Moroccan license plates
taken from different real-world scenarios, with the addition of
various data augmentation techniques, to ensure robustness and
efficiency under different conditions. To improve the generalization
ability and accuracy of our system, we also combined
the proposed algorithm with the transfer learning technique.
Extensive experiments prove that the proposed method achieves
an excellent trade-off between speed and accuracy as well as
the system executes the detection /recognition process in a
single phase with 98.45% of accuracy and 59.5 Frames Per
Second (FPS). Also, a comparative evaluation demonstrates the
effectiveness of the proposed method compared to previous stateof-
the-art methods.
Index Terms—Real time Automatic License Plate Recognition,
Deep learning, Transfer learning, Data augmentation; Tiny-
YOLOv3