Enhancement of Blurred Indonesian License Plate Number Identification Using Multi-Scale Information CNN

One of the challenges in traffic surveillance and public safety in Indonesia is the recognition of blurred vehicle license plates. In this case, the development of image processing technology and artificial intelligence is key in improving the ability of license plate recognition systems to overcome the obstacles of blurred images. This research aims to develop a license plate recognition method that can work effectively in overcoming the problem of blurred license plate recognition and improve accuracy. This research will use advanced and accurate image processing technique technology by utilizing multiple layers, such as Convolutional Neural Network (CNN), Multi-scale Information CNN (I-CNN), Number Plate Segmentation, and Transfer learning. In addition, the proposed number plate recognition method is able to cope with blurred images and variations in the characteristics of number plates in the form of different letters and numbers throughout Indonesia. In this study, we used the License Plate Digits Classification (LPDC) dataset consisting of 35-character classes. Each class consists of 1000–1030 photos from different angles and lighting conditions. The dataset we tested was an Indonesian license plate, consisting of 549 blur datasets and 255 normal datasets. By using categorical cross entropy to determine the accuracy in this research, we get the results of CNN training accuracy of 85,73% and I-CNN training accuracy of 97,69%.

I. Introduction

The increase in population in Indonesia has resulted in an increase in the number of motorised vehicle users. The use of motorised vehicles has grown exponentially, which has contributed to an increase in road traffic violations. Data from Indonesia's Central Bureau of Statistics shows that from 2019 to 2021 all types of motorised vehicles have increased [1]. This condition causes various problems such as air pollution, increased accident cases, congestion and traffic violations. Data from Traffic Corps of the Indonesian National Police [2] shows that the number of traffic violations recorded through Electronic Traffic Law Enforcement (ETLE) reached 512.9 thousand vehicles during January-May 2023. ETLE takes action against violations automatically through monitoring cameras. Vehicle object detection, image processing on number plates, and pattern recognition on number plates are techniques to recognise number plates [3]. The designed system can be widely used for many purposes, such as automatic toll management system on highways, number plate validation, stolen vehicle detection, and traffic statistics.