A fast instance segmentation with one-stage multi-task deep neural network for autonomous driving. (July 2021)

• Record Type: Journal Article
• Authors: Tseng, Kuo-Kun; Lin, Jiangrui; Chen, Chien-Ming; Hassan, Mohammad Mehedi
• Is Part Of: Computers & electrical engineering. Volume 93(2021)
• Publication Date: 2021-07
• Subjects: Instance segmentation -- Autonomous driving -- Multi-task neural network -- Object detection -- Image segmentation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107194
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: An accurate real-time instance segmentation, which can perform both object detection and semantic segmentation at the same time with a multi-task neural network, is important for autonomous driving. This paper proposes a fast one-stage multi-task neural network for instance segmentation, which can meet the requirements of real-time processing with sufficient accuracy and that is more desirable for self-driving applications. With a one-stage strategy, it can perform object detection and segmentation concurrently. This paper conducts the related experiments with two public datasets. The cross-validation was carried out with set of variables to determine the optimal combination of each model and compared with the mainstream instance segmentation algorithms. According to our experiment, the proposed algorithm has five times the performance compared to the previous algorithms, which can meet the real-time requirement for autonomous driving applications.