License Plate Identification from Myanmar Vehicle Images under Different Environmental Conditions

Abstract

We have developed a license plate identification method for Myanmar vehicles that are captured under dissimilar conditions, e.g., angle of image capturing, different types of license plates, and real environmental conditions. In this study, car license plate recognition (CLPR), bounding box, horizontal and vertical dilations, skew angle detection, and plate detection were proposed to identify license numbers from different vehicle images. To recognize the characters, a new algorithm based on deep learning, a subset of artificial intelligence (AI), is proposed. The neural nets are progressing rapidly in many fields. The applied model of neural network is used for classification. The recognition part is a very challenging task. Compared with the traditional method, the neural network has obvious advantages. The benefit of this research is to eliminate the need of license plate recognition (LPR) under different conditions. In mobile phones, there are many sensors used to detect the presence of nearby objects. Accelerometers in mobile phones are used. Developed for the Samsung mobile phone, sensors can yield sensor readings but it not much else. Each car was viewed from four different angles under different conditions. In our experiment, the results showed an average accuracy of 97%, which was substantially applied to license plate identification under different environmental conditions. To extend the experiment, the vehicle images were also collected under different conditions, such as dark and cloudy weather and various sizes and positions of plates.

An Intelligent IoT-based Vision System for Nighttime Vehicle Detection and Energy Saving

“…In general, advanced cities will eventually have to confront the problem of traffic congestion, which could be alleviated through a system that accurately delivers real-time traffic data. (19,20) As vehicle detection technology is approaching maturity, various methods are now available and are distinguished primarily by cost. At present, nighttime vehicle detection technology is relatively less mature than its daytime counterpart, since nighttime factors such as the lack of light and road reflections on rainy days have created detection issues that increase the difficulty of nighttime detection.…”