Abstract
Recently, deep learning networks with high performance for object recognition are emerging. In the case of object recognition using deep learning, it is important to build a training data set to improve performance. To build a data set, we need to collect and label the images. This process requires a lot of time and manpower. For this reason, open data sets are used. However, there are objects that do not have large open data sets. One of them is data required for license plate detection and recognition. Therefore, in this paper, we propose an artificial license plate generator system that can create large data sets by minimizing images. In addition, the detection rate according to the artificial license plate arrangement structure was analyzed. As a result of the analysis, the best layout structure was FVC_III and B, and the most suitable network was D2Det. Although the artificial data set performance was 2-3% lower than that of the actual data set, the time to build the artificial data was about 11 times faster than the time to build the actual data set, proving that it is a time-efficient data set building system.
| Translated title of the contribution | 2D Artificial Data Set Construction System for Object Detection and Detection Rate Analysis According to Data Characteristics and Arrangement Structure: Focusing on vehicle License Plate Detection |
|---|---|
| Original language | Korean |
| Pages (from-to) | 185-197 |
| Number of pages | 13 |
| Journal | 방송공학회 논문지 |
| Volume | 27 |
| Issue number | 2 |
| DOIs | |
| State | Published - Mar 2022 |