MIFNet: A lightweight multiscale information fusion network

International Journal of Intelligent SystemsVolume 37, Issue 9 p. 5617-5642 RESEARCH ARTICLE MIFNet: A lightweight multiscale information fusion network Jieren Cheng, Jieren Cheng School of Computer Science and Technology, Hainan University, Haikou, China Hainan Blockchain Technology Engineering Research Center, Haikou, ChinaSearch for more papers by this authorXin Peng, Corresponding Author Xin Peng [email protected] orcid.org/0000-0001-8642-8582 School of Cyberspace Security, Hainan University, Haikou, China Correspondence Xin Peng, School of Cyberspace Security, Hainan University, 570228 Haikou, China. Email: [email protected]Search for more papers by this authorXiangyan Tang, Xiangyan Tang School of Computer Science and Technology, Hainan University, Haikou, China College of Intelligence and Computing, Tianjin University, Tianjin, ChinaSearch for more papers by this authorWenxuan Tu, Wenxuan Tu College of Computing, National University of Defense Technology, Changsha, ChinaSearch for more papers by this authorWenhang Xu, Wenhang Xu School of Cyberspace Security, Hainan University, Haikou, ChinaSearch for more papers by this author Jieren Cheng, Jieren Cheng School of Computer Science and Technology, Hainan University, Haikou, China Hainan Blockchain Technology Engineering Research Center, Haikou, ChinaSearch for more papers by this authorXin Peng, Corresponding Author Xin Peng [email protected] orcid.org/0000-0001-8642-8582 School of Cyberspace Security, Hainan University, Haikou, China Correspondence Xin Peng, School of Cyberspace Security, Hainan University, 570228 Haikou, China. Email: [email protected]Search for more papers by this authorXiangyan Tang, Xiangyan Tang School of Computer Science and Technology, Hainan University, Haikou, China College of Intelligence and Computing, Tianjin University, Tianjin, ChinaSearch for more papers by this authorWenxuan Tu, Wenxuan Tu College of Computing, National University of Defense Technology, Changsha, ChinaSearch for more papers by this authorWenhang Xu, Wenhang Xu School of Cyberspace Security, Hainan University, Haikou, ChinaSearch for more papers by this author First published: 29 December 2021 https://doi.org/10.1002/int.22804Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Semantic segmentation technique plays a crucial role in Internet of Things applications, such as industrial robotics and self-driving. Recently deep learning approaches have boosted semantic segmentation accuracy greatly. However, their comprehensive performance in terms of accuracy and efficiency is still far from satisfactory. We observe that (1) accuracy-oriented methods rely on numerous convolution layers and sophisticated architectures, which result in heavy computational complexity and usually take a long time for inference; (2) efficiency-oriented methods fail to capture the multiscale context information for discriminative representations during the feature fusion process, thus leading to suboptimal performance. Previous semantic segmentation approaches fail to address these two challenges simultaneously. To tackle the dilemma of precise segmentation and efficient inference, we propose a novel lightweight Multiscale Information Fusion Network (MIFNet). Specifically, the proposed MIFNet mainly consists of two core components, that is, Pyramid Refinement Connection Module (PRCM) and Lightweight Information Fusion Module (LIFM). The PRCM exploits skip learning to establish dependency between different stages. Meanwhile, the pyramid attention mechanism (PAM) in PRCM, which adjusts the weight of hybrid pyramid attention vector to refine spatial features of low-level, is developed to alleviate the semantic gap. Moreover, the LIFM is designed to detect objects at multiple scales from the global-local perspective. In LIFM, the proposed multiscale dense concatenation (MDC) adopts various dilated convolution to extract multiscale local context information. Extensive experimental results on benchmarks data sets demonstrate the significantly better performance of the proposed MIFNet compared with most existing state-of-the-art methods. Citing Literature Volume37, Issue9September 2022Pages 5617-5642 RelatedInformation