지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.7 No.5
- 발행연도
- 2018.10
- 수록면
- 342 - 349 (8page)
- DOI
- 10.5573/IEIESPC.2018.7.5.342
이용수
초록· 키워드
오류제보하기
In a bad weather environment, the existing road object–detection algorithms using deep learning seldom properly detect road objects in fog, rain, and/or snow. This is due to the lack of data on bad weather environments in the well-known training data, but there is little or no bad weather data that can actually be employed. In this paper, we propose a method to synthesize rain in road images as training data for road object detection in bad weather environments. The proposed method is composed of fog synthesis and rain streak generation. The fog synthesis step estimates and corrects the fog transmission value using depth information from a clear image, and expresses a fog-like feature of the real road image through Gaussian filtering and temporal filtering. We also generate rain streaks using the Unity3D program. We experimented with detecting road objects by learning synthesized rainy images and original rain-free images before synthesis, and compared the two results. Thus, we confirmed that learning with rain-synthesized images improves the detection rate by up to 53%. Experimental results also show that the proposed method can improve vehicle detection performance by about 3.5%, even with real, rainy videos.
목차
Abstract
1. Introduction
2. Related Works
3. Proposed Method
4. Experimental Results
5. Conclusion
References
참고문헌 (14)
참고문헌 신청
A. Geiger / 2012 / Are we ready for autonomous driving? the KITTI vision benchmark suite / IEEE Conference on Computer Vision and Pattern Recognition
W. Ren / 2016 / Single image dehazing via multi-scale convolutional neural networks / European Conference on Computer Vision
F. Guo / 2014 / Foggy scene rendering based on transmission map estimation / International Journal of Computer Games Technology 2014(10)
N. Zhang / 2017 / Towards simulating foggy and hazy images and evaluating their authenticity / International Conference on Neural Information Processing 10636:405~415
X. Fu / 2017 / Clearing the skies: a deep network architecture for single-image rain removal / IEEE Transactions on Image Processing 26(6):2944~2956
W. Ren / 2016 / Single image dehazing via multi-scale convolutional neural networks / European Conference on Computer Vision
F. Guo / 2014 / Foggy scene rendering based on transmission map estimation / International Journal of Computer Games Technology 2014(10)
N. Zhang / 2017 / Towards simulating foggy and hazy images and evaluating their authenticity / International Conference on Neural Information Processing 10636:405~415
X. Fu / 2017 / Clearing the skies: a deep network architecture for single-image rain removal / IEEE Transactions on Image Processing 26(6):2944~2956
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UCI(KEPA) : I410-ECN-0101-2019-569-000114473