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http://dx.doi.org/10.22156/CS4SMB.2020.10.08.040

Yolo based Light Source Object Detection for Traffic Image Big Data Processing  

Kang, Ji-Soo (Department of Computer Science, Kyonggi University)
Shim, Se-Eun (Division of Computer Science and Engineering, Kyonggi University)
Jo, Sun-Moon (Department of Computer Information Technology Education, Paichai University)
Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
Publication Information
Journal of Convergence for Information Technology / v.10, no.8, 2020 , pp. 40-46 More about this Journal
Abstract
As interest in traffic safety increases, research on autonomous driving, which reduces the incidence of traffic accidents, is increased. Object recognition and detection are essential for autonomous driving. Therefore, research on object recognition and detection through traffic image big data is being actively conducted to determine the road conditions. However, because most existing studies use only daytime data, it is difficult to recognize objects on night roads. Particularly, in the case of a light source object, it is difficult to use the features of the daytime as it is due to light smudging and whitening. Therefore, this study proposes Yolo based light source object detection for traffic image big data processing. The proposed method performs image processing by applying color model transitions to night traffic image. The object group is determined by extracting the characteristics of the object through image processing. It is possible to increase the recognition rate of light source object detection on a night road through a deep learning model using candidate group data.
Keywords
Traffic Safety; Deep Learning; Object Detection; Light Source Object; Image Processing;
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