SGNet: a fast and accurate semantic segmentation network based on semantic guidance

ABSTRACTIn this paper, we design a fast and accurate lightweight semantic segmentation method for mobile robots. Accurate semantic segmentation usually requires obtaining high-resolution feature maps with strong semantic representation. Among the widely utilized methods, dilated convolution is computationally intensive and the feature pyramid accuracy is not as accurate. Inspired by the Flow Alignment Module (FAM) proposed by SFNet, we propose a Semantic Guided Upsampling Module (SGUM) for learning semantic offsets between neighboring level feature maps to solve the semantic misalignment problem and effectively fuse high level semantic features into low-level high-resolution feature maps. The integration of the SGUM into the semantic segmentation network with different backbones shows better performance than other methods on various datasets, showing the effectiveness of the SGUM on semantic feature fusion as well as its generalizability.KEYWORDS: Semantic segmentationsemantic guided upsamplingfeature pyramid Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work described in this paper was supported by the National Natural Science Foundation of China (No. 61991415, 62073209), the Jiangxi Province Key R&D Program (No. 20212BBE53028) and the Shanghai Commission of Science and Technology Program (No. 23010500100).Notes on contributorsHengyu LiHengyu Li received the B.S. degree in mechanical engineering and automation from Henan Polytechnic University, China, in 2006, and the M.S. and Ph.D. degrees in mechanical and electronic engineering from Shanghai University, China, in 2009 and 2012, respectively, where he is currently an Associate Professor with the School of Mechatronic Engineering and Automation. His research interests include mechatronics and robot bionic vision systems and autonomous cooperative control for multiple robots.Shuang HanShuang Han received the B.S. degree in mechanical engineering and automation from Shanghai University, Shanghai, China, in 2019, where he is currently working toward the M.S. degree in mechatronic engineering with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. His research interests include computer vision, simultaneous localization and mapping (SLAM) and deep learning.Jingyi LiuJingyi Liu received the B.S. degree in mechanical engineering and automation, the M.S. and Ph.D. degrees in mechatronic engineering from Shanghai University, Shanghai, China, in 2014, 2017 and 2022, respectively, where he is currently a Postdoctoral Researcher with the School of Mechatronic Engineering and Automation. His research interests include computer vision, machine learning, automation control, and perception for mobile robotics.Hongzhou WangHongzhou Wang received his bachelor's and master's degrees in 2009 and 2012, respectively, and is currently pursuing a doctoral degree at Shanghai University. His research areas include the science of mechanisms and robotics.Yueying WangYueying Wang received the B.S. degree in mechanical engineering and automation from the Beijing Institute of Technology, Beijing, China, in 2006, the M.S. degree in navigation, guidance, and control, and the Ph.D. degree in control science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 2010 and 2015, respectively. He is currently an Associate Professor with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai. His current research interests include intelligent and hybrid control systems and control of unmanned aerial/surface vehiclesShaorong XieShaorong Xie received the B.S. and M. S. degrees in mechanical engineering from Tianjin Polytechnic University, Tianjin, China, in 1995 and 1998, respectively, and the Ph.D. degree in mechanical engineering from the Institute of Intelligent Machines at Tianjin University and the Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China, in 2001. She is a Professor with the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. Her research areas include advanced robotics technologies, bionic control mechanisms of eye movements, and image monitoring systems.