MACHINE PERCEPTION AND ROBOTICS GROUP

Dept. of Robotics Science and Technology,
Chubu University

Multi-task learning is effective for object detection and segmentation, which are closely related to each other and necessary for automated driving. However, there is a problem with the learning process in conventional multi-task learning models. In multi-task learning, common features among downstream tasks are first extracted by a backbone network. Then, these features are used for different downstream tasks. Since the required feature is different depending on the downstream task, it is necessary to extract features suitable for each downstream task. In this paper, we propose a multi-tasking model that introduces BiFPN feature fusion method for automated driving tasks and the Next-ViT model utilizing CNN and Transformer to extract features. From the evaluation experiments of automated driving tasks, we confirmed that the proposed method improves the accuracy of multi-task learning.