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악천후 환경에 강건한 혼합밀도네트워크 기반 객체 탐지 딥러닝 모델
조택형(Taekhyung Cho),최종은(Jongeun Choi) 한국자동차공학회 2023 한국자동차공학회 부문종합 학술대회 Vol.2023 No.5
With the growth of deep learning technology, there are many elaborate object detection models being developed for safe autonomous driving. However, a common problem is that the training data is often biased toward normal daytime which leads to high uncertainty in the predictions on adverse weather conditions that were not included in the training data. Therefore, in this paper, we developed a robust model for bad weather conditions by utilizing mixture density network to estimate the uncertainty of the deep learning model’s predictions. Our method showed better performance than original models in fog, rain, and nighttime environments.