文献类型：期刊文献

英文题名：Research on the Application of Semantic Segmentation of driverless vehicles in Park Scene

作者：Ren, Lijun[1]; Liu, Yuansheng[2]

第一作者：Ren, Lijun

机构：[1] Beijing Key Laboratory of Information, Service Engineering, Beijing Union University, Beijing, China; [2] College of Robotics, Beijing Union University, Beijing, China

第一机构：北京联合大学

年份：2020

起止页码：342-345

外文期刊名：Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020

收录：EI(收录号：20210609888675)

语种：英文

外文关键词：Image enhancement - Semantics

摘要：Semantic segmentation is widely used in the establishment of semantic map, but a good semantic segmentation model still has the disadvantage of insufficient real-time performance for driverless applications, especially in the park. In this paper, an improved PFPN (Panoptic Feature Pyramid Network) network model is proposed to reduce the time of semantic segmentation. In this algorithm, the instance segmentation branch function in the original PFPN is trimmed and the small target feature layer extracted from the semantic segmentation branch is pruned, so as to reduce the semantic segmentation time of the model. In order to verify the correctness and feasibility of the model, a test environment is set up in the campus scene. The experimental results show that the improved lightweight semantic segmentation model achieves better segmentation results, and the average image segmentation time is reduced by 22.2%. ?2020 IEEE