부산대학교 도서관

In the field of autonomous driving, many models based on deep learning methods have been constructed to solve computer vision tasks related to this domain, such as object detection, semantic segmentation, and depth estimation. Each model can provide different information about the surrounding environment of a vehicle to assist in driving. However, for applying in the real world, more detailed information about the surrounding environment is essentially required. In this study, we propose a model based on the concept of multi-task learning. This model is an encoder-decoder architecture mainly consisting of an encoder using the hard-parameter sharing technique and three decoders for individual tasks. Therefore, this model can handle object detection, semantic segmentation, and depth estimation at the same time. Our proposed multi-task model has been verified to perform well on the public dataset Cityscapes and has higher generalizability than other models.