LICENSE PLATE RECOGNITION SYSTEM BASED ON MASK R-CNN

Abstract

Automatic license plate recognition (ALPR) systems can be found in many different applications. If a person has a driving license, the person probably had already seen smart road camera after a speed limit sign or on a crossroad. Every year number of cars on roads growth very fast. It is also obvious that such systems can be used out-of-road situations. For instance, this type of systems can be used for automatic access control on private property or smart parking, or even log system that are being used literally everywhere. Because of popularity of ALPR systems, there are two main goals, which are being pursued by researches: speed and accuracy of recognition. Speed of the detection is important for real-time systems. Accuracy is important for every system. The more accurate a system is, the more reliable it is. For example, car accident detection systems should be as accurate as possible in order to be used, because no one wants to get billed with the wrongdoing, that wasn’t committed by them. The purpose of the study is to develop high precision automatic license plate detection system with number extraction possibilities. In order to achieve the goal many different modern solutions and technologies were studied and solution is presented. The main technology of the project is artificial intelligence system and, more specifically, convolutional neural network. As the main algorithm Mask R-CNN is used for license plate detection. To present reasonable research, the system was tested on different hardware (CPU, GPU, Raspberry PI 4) and on different datasets.

Keywords

• automatic license plate recognition,
• machine learning,
• deep learning,
• convolutional neural networks,
• mask r-cnn