상세검색
비밀번호 변경 안내
비밀번호를 변경하신 지 90일 이상 지났습니다.
개인정보 보호를 위해 비밀번호를 변경해 주세요.
비밀번호 변경 안내
비밀번호를 변경하신 지 90일 이상 지났습니다.
개인정보 보호를 위해 비밀번호를 변경해 주세요.
DOI : 10.5573/IEIESPC.2018.7.5.342
UCI(KEPA) : I410-ECN-0101-2019-569-000114473
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.
Abstract
1. Introduction
2. Related Works
3. Proposed Method
4. Experimental Results
5. Conclusion
References
도움이 되었어요.0
도움이 안되었어요.0
알림 설정하기
논문 오류신고
신고항목
이 논문의 참고문헌을 찾아주세요.
이 논문의 참고문헌을 찾아주세요.
구매하기
장바구니
인용양식
공식 스폰서와 앰부시 마케팅의 광고 크리에이티브 효과 : 2009 광저우 아시안게임을 중심으로
기관인증