Using Super-Resolution Imaging for Recognition of Low-Resolution Blurred License Plates: A Comparative Study of Real-ESRGAN, A-ESRGAN, and StarSRGAN
This addresses license plate recognition issues in scenarios like road monitoring in Taiwan, where poor camera quality reduces accuracy, but it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of recognizing low-resolution blurred license plates by fine-tuning and comparing three super-resolution models (Real-ESRGAN, A-ESRGAN, and StarSRGAN) to enhance photo resolution and improve recognition accuracy, but no concrete numbers on results were provided.
With the robust development of technology, license plate recognition technology can now be properly applied in various scenarios, such as road monitoring, tracking of stolen vehicles, detection at parking lot entrances and exits, and so on. However, the precondition for these applications to function normally is that the license plate must be 'clear' enough to be recognized by the system with the correct license plate number. If the license plate becomes blurred due to some external factors, then the accuracy of recognition will be greatly reduced. Although there are many road surveillance cameras in Taiwan, the quality of most cameras is not good, often leading to the inability to recognize license plate numbers due to low photo resolution. Therefore, this study focuses on using super-resolution technology to process blurred license plates. This study will mainly fine-tune three super-resolution models: Real-ESRGAN, A-ESRGAN, and StarSRGAN, and compare their effectiveness in enhancing the resolution of license plate photos and enabling accurate license plate recognition. By comparing different super-resolution models, it is hoped to find the most suitable model for this task, providing valuable references for future researchers.