Data Augmentation based on Deep Learning for Object Detection of Infrared Cameras in Extreme Environments극한환경에서 적외선 카메라의 객체 탐지를 위한 딥러닝 기반 데이터 증강 방법에 관한 연구

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Recently, in-depth studies on sensors of autonomous vehicles have been conducted. In particular, the trend to pursue only camera-based autonomous driving is progressing. Studies on object detection using IR (Infrared) cameras is essential in overcoming the limitations of the VIS (Visible) camera environment. Deep learning-based object detection technology requires sufficient data, and data augmentation can make the object detection network more robust and improve performance. In this paper, a method to increase the performance of object detection by generating and learning a highresolution image of an infrared dataset, based on a data augmentation method based on a Generative Adversarial Network (GAN) was studied. We collected data from VIS and IR cameras under severe conditions such as snowfall, fog, and heavy rain. The infrared data images from KAIST were used for data learning and verification. We confirmed that the proposed data augmentation method improved the object detection performance, by applying generated dataset to various object detection networks. Based on the study results, we plan on developing object detection technology using only cameras, by creating IR datasets from numerous VIS camera data to be secured in the future and fusion with VIS cameras.
Publisher
Korean Society for Precision Engineeing
Issue Date
2022-06
Language
Korean
Article Type
Article
Citation

Journal of the Korean Society for Precision Engineering, v.39, no.6, pp.387 - 394

ISSN
1225-9071
DOI
10.7736/JKSPE.022.026
URI
http://hdl.handle.net/10203/302717
Appears in Collection
ME-Journal Papers(저널논문)
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