Abstract
The number and range of the candidate vehicle license plate (VLP) region affects the result of the VLP extraction symmetrically. Therefore, in order to improve the VLP extraction rate, many candidate VLP regions are selected. However, there is a problem that the processing time increases symmetrically. In this paper, we propose a method that allows detecting a vehicle license plate in the real-time mode. To do this, the proposed method makes use of the region-based convolutional neural network (R-CNN) method and morphological operations. The R-CNN method is a deep learning method that selects a large number of candidate regions from an input image and compares them to determine whether objects of interest are included. However, this method has limitations when used in real-time processing. Therefore, to address this limitation in the proposed method, while selecting a candidate vehicle region, the selection range is reduced based on the size and position of the vehicle in the input image; hence, processing can be performed quickly. A vehicle license plate is detected by performing a morphological operation based on the edge pixel distribution of the detected vehicle region. Experimental results show that the detection rate of vehicles is approximately 92% in real road environments, and the detection rate of vehicle license plates is approximately 83%.
Highlights
It technology is applied symmetrically for autonomous vehicle safety
region-based convolutional neural network (R-CNN), detection time increases according toaccording the searchtorange of the using the R-CNN, the detection processing time increases symmetrically according to the search range image
We proposed the method, which implies using black-box images from vehicles to detect other vehicles in their vicinity and extract the license plate areas of these vehicles
Summary
It technology is applied symmetrically for autonomous vehicle safety. The application of many safety technologies to autonomous vehicles has a symmetrical meaning that the safety of the driver can be guaranteed. Level 2 means that semiautonomous driving is possible by combining functions. The ADAS function, driving assistance function, and vehicle distance maintenance function are included in this option [8]. Most of these functions are currently only possible by combining hardware sensors and vision sensors. Improved environmental perception is possible by attaching these sensors to the vehicle These sensors increase the price of vehicles, and they frequently fail or need to be replaced due to slight accidents
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