Abstract:
(ALPR) Automatic License Plate identification plays a crucial role in traffic management,
security applications, tools collection and various aspects of modern life. Every country
has their own unique license plate model. For this reason, in every country, we need to use
different dataset containing their license plate and different models to develop license plate
identification. This research explores the application of YOLOv9, which are a powerful
deep learning model, for real-time Identification of Bangladeshi license plates and it is so
fast and efficient. The proposed approach involves training YOLOv9 on a comprehensive
dataset, which has a large number of Bangladeshi license plates images of various angle,
various lighting condition and various weather condition taken by mobile phone camera by
me. This proposed system addresses the specific challenges associated with Bangladeshi
license plates, including their unique format, classes, types and potential variations in
lighting and environmental conditions. As of my knowledge there are no robust dataset
available for this research. I have collected more than 4000 Bangladeshi license plate
images with various angles, weather conditions and different lighting conditions.
Importantly, I have collected all the classes and types of Bangladeshi license plate images
for robust recognition. I train the dataset with YOLOv8m & YOLOv9c framework. YOLO
(You Only Look Once) is known for fastest and efficient recognition of small characters
like license plate. My proposed model achieved 96.54% accuracy for detecting all types of
license plate in Bangladesh including Army, Navy, Air-force. There are existing papers
available there has up to 95% accuracy rate for detecting license plate although they did
not use all the classes and types of images of Bangladeshi license plates. So, I used all the
types and classes of images for robust detection of Bangladeshi license plate. The proposed
solution improves ALPR by including YOLOv9, a cutting-edge deep learning model, to
enable rapid and accurate license plate identification. For recognition characters I used
EasyOCR technique and I achieved 86.67% accuracy of identification the characters. My
proposed approach has the potential to transform traffic management, security, and
applications in Bangladesh, allowing for automated toll collection, improved traffic
analysis, and enhanced security measures.
