Abstract
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.
Abstract (translated)
随着技术的稳健发展，现在可以在各种场景中正确应用车牌识别技术，如道路监控、追踪被盗车辆、入口和出口的检测等。然而，这些应用正常运行的先决条件是车牌必须足够清晰，以便系统能够正确识别正确的车牌号码。如果车牌因一些外部因素变得模糊，那么识别的准确性将大大降低。尽管台湾有很多道路监控摄像头，但大多数摄像头的质量并不好，通常导致由于低照片分辨率无法识别车牌号码。因此，本研究将专注于使用超分辨率技术处理模糊的车牌。本研究将主要优化三种超分辨率模型：Real-ESRGAN、A-ESRGAN和StarSRGAN，并比较它们在提高车牌照片分辨率并实现准确车牌识别的有效性。通过比较不同超分辨率模型，希望找到最适合这项任务的模型，为未来研究者提供宝贵的参考。
URL
https://arxiv.org/abs/2403.15466
