Detecting Vehicle Traffic Violations Using Deep Learning Using Retina-LPD and GFP-GAN

In the age of technology, the use of technological devices has become an indispensable part of daily life. It combines technological devices with artificial intelligence models to exploit their potential. Combining traffic surveillance cameras with artificial intelligence is receiving attention and research from countries worldwide. To continue this research, we use surveillance cameras combined with artificial intelligence to build a system to identify and authenticate violating vehicles. However, applying the system to different environmental conditions is always challenging. This system uses deep learning models and image processing techniques that require fast speed and high reliability. The proposed method for this system is to use the Retina-LPD and GFP-GAN models to improve the license plate recognition model, add license plate alignment methods, and build an additional image quality recovery model. height from the license plate is aligned to help increase model reliability. The system is deployed on the ICOMM server, and evaluated on video from traffic surveillance cameras from Hanoi city surveillance cameras. The system has achieved better results than the old system up to 99.74%, helping to increase system reliability. Although the system has made a lot of progress in vehicle authentication, many challenges and issues must be resolved to ensure effective performance and response to all environmental conditions.