A scene segmentation algorithm combining the body and the edge of the object |
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| Institution: | 1. School of Information and Communication Engineering, Hunan Institute of Science and Technology, Hunan, China;2. Machine Vision & Artificial Intelligence Research Center, Hunan Institute of Science and Technology, Hunan, China;1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China;2. School of Information and Library Science, University of North Carolina at Chapel Hill, NC 27599, USA;3. College of Computing and Informatics, Drexel University, Philadelphia, PA 19104, USA;4. School of Medicine, University of North Carolina at Chapel Hill, NC 27516, USA |
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| Abstract: | Scene segmentation is a very challenging task where convolutional neural networks are used in this field and have achieved very good results. Current scene segmentation methods often ignore the internal consistency of the target object, and lack to make full use of global and local context information which leads to the situation of object misclassification. In addition, most of the previous work focused on the segmentation of the main part of the object, however, there are few researches on the quality of the object edge segmentation. In this article, based on the use of flow information to maintain body consistency, the context feature extraction module is designed to fully consider the global and local body context information of the target object, refining the rough feature map in the intermediate stage. So, the misclassification of the target object is reduced. Besides, in the proposed edge attention module, the low-level feature map guided by the global feature and the edge feature map with semantic information obtained by intermediate process are connected to obtain more accurate edge detail information. Finally, the segmentation quality that contains the body part of the noise and the edge details can be improved. This paper not only conducts experiments on the classic FCN, PSPNet, and DeepLabv3+ several mainstream network architectures, but also on the real-time SFNet network structure proposed last year, and the value of mIoU in object and boundary is improved to verify the effectiveness of the method proposed in this paper. Moreover, in order to prove the robustness of the experiment, we conduct experiments on three complex scene segmentation data sets of Cityscapes, CamVid, and KiTTi, and obtained mIoU values of 80.52% on the Cityscapes validation data set, and 71.4%, 56.53% on the Camvid and KITTI test data set, which shows better results when compared with most of the state-of-the-art methods. |
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