Toward Improving Robustness of Object Detectors Against Domain Shift
Le-Anh Tran Chung Nguyen Tran Dong-Chul Park Jordi Carrabina David Castells-Rufas
This paper proposes a data augmentation method for improving the robustness
of driving object detectors against domain shift. Domain shift problem arises
when there is a significant change between the distribution of the source data
domain used in the training phase and that of the target data domain in the
deployment phase. Domain shift is known as one of the most popular reasons
resulting in the considerable drop in the performance of deep neural network
models. In order to address this problem, one effective approach is to increase
the diversity of training data. To this end, we propose a data synthesis module
that can be utilized to train more robust and effective object detectors. By
adopting YOLOv4 as a base object detector, we have witnessed a remarkable
improvement in performance on both the source and target domain data. The code
of this work is publicly available at
https://github.com/tranleanh/haze-synthesis.