Two-step Domain Adaptive Semantic Segmentation Algorithm of Driving Scene

Abstract

Semantic segmentation of driving scenes is an important task in the field of automatic driving perception. The existing semantic segmentation based on deep learning method needs a lot of manpower cost to label data, and the changeable driving scenes will also lead to the performance degradation of semantic segmentation model in practical application. Therefore, in this paper, labeled computer-generated images are used as source domain data, unlabeled real driving scene images are used as target domain data, and unsupervised domain adaptive method is used to transfer knowledge when the target domain data is unlabeled. Based on the one-step domain adaptive semantic segmentation algorithm, a two-step domain adaptive semantic segmentation algorithm is proposed, and an image quality evaluation module is introduced to optimize the algorithm, so as to improve the performance index of cross-domain semantic segmentation tasks to some extent and reduce the dependence on data labeling and labeling cost.