| dc.description.abstract | This study focuses on utilizing deep learning neural networks to perform license plate detection and character recognition. The primary approach employed in this research is the training and utilization of YOLOv7 (You Only Look Once V7) for object detection. Compared to previous prominent real-time object detection techniques like YOLOv5 and EfficientDet, YOLOv7 offers a remarkable reduction of 40% in parameter count and 50% in computational load while maintaining faster inference speeds and improved precision.
Consequently, YOLOv7 was chosen as the preferred model for this study.Given that conventional license plate recognition methods often involve preprocessing steps such as binarization, erosion filtering, and are limited by fixed angles, lighting conditions, and positions, this research aims to address these limitations by leveraging super-resolution models and low-light algorithms in conjunction with YOLOv7. The objective is to achieve fast and robust license plate recognition across diverse environmental conditions. The experimental dataset comprises a variety of street scenes featuring different vehicles at varying distances. In total, there are 5,524 images for license plate training and 4,676 images for character training. The test dataset consists of 300 images that encompass various complex environments such as distant views, tilting, blurriness, dim lighting, and more. The accuracy of license plate localization reaches 98.5%, while for reasonably captured plates within a three-meter range, it exceeds 99%. The character recognition accuracy is 99.3%. Additionally, the recall for license plate localization is 98.1%, and the F1-Score is 98.3%. For character recognition, the recall is 98.6%, and the F1-Score is 98.95%.
As YOLOv7 utilizes the PyTorch deep learning framework, efforts were made to ensure compatibility with mobile devices, necessitating model lightweighting to alleviate the computational burden on Central Processing Unit (CPU). Consequently, the models underwent conversion across multiple deep learning frameworks, including Open Neural Network Exchange (ONNX), TensorFlow (TF), and TensorFlow Lite (TFLite).Finally, an Android Studio application was developed to deploy the system, achieving a 99% accuracy in license plate recognition for license plates reasonably captured within a three-meter range, and enabling real-time detection on an Android mobile industrial computer. | en_US |