Названия:
REAL-TIME OBJECT DETECTION FOR AUTOMATED CAR NUMBER PLATE RECOGNITION AND IMAGE SYNTHESIS
Автор:
Keyik Allaberdiyyeva, Enejan Amanova, Gulnar Sahetdurdyyeva, Rahman Myradov, Sapartach Hojabalkanova
Расположение страниц:
162-167
Язык:
Английский
Аннотация:
This study explores the application of real-time object detection algorithms in automated car number plate recognition (ANPR) systems. The proposed method focuses on improving the accuracy, efficiency, and speed of number plate recognition using convolutional neural networks (CNNs) and image synthesis techniques. The research discusses the integration of deep learning models for object detection, as well as the use of synthetic data to train models for recognizing car number plates in diverse real-world conditions. Additionally, this study presents the impact of image synthesis on enhancing model robustness and reducing the need for large annotated datasets. The results demonstrate the effectiveness of real-time detection systems in various traffic environments.