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도시내 다양한 야간 조명 환경에 강인한 자율주행 알고리즘 개발을 위한 데이터 증강 기법
강정규(Jungyu Kang),안택현(Taeghyun Ahn),민경욱(Kyoung-Wook Min) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
Recently, datasets for training a CNN model have become very important in the field of self-driving vehicle research. However, most of the currently released deep learning datasets are focused on daytime environments. In this paper, we present a study on how to enhance the robustness of the CNN model against night environment by augmenting the autonomous driving dataset using CycleGAN. The experimental results showed that the proposed framework shows a significant improvement in robustness without adding any additional annotated datasets.