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Supervised semantic segmentation based on deep learning: a survey

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Abstract

Recently, many semantic segmentation methods based on fully supervised learning are leading the way in the computer vision field. In particular, deep neural networks headed by convolutional neural networks can effectively solve many challenging semantic segmentation tasks. To realize more refined semantic image segmentation, this paper studies the semantic segmentation task with a novel perspective, in which three key issues affecting the segmentation effect are considered. Firstly, it is hard to predict the classification results accurately in the high-resolution map from the reduced feature map since the scales are different between them. Secondly, the multi-scale characteristics of the target and the complexity of the background make it difficult to extract semantic features. Thirdly, the problem of intra-class differences and inter-class similarities can lead to incorrect classification of the boundary. To find the solutions to the above issues based on existing methods, the inner connection between past research and ongoing research is explored in this paper. In addition, qualitative and quantitative analyses are made, which can help the researchers to establish an intuitive understanding of various methods. At last, some conclusions about the existing methods are drawn to enhance segmentation performance. Moreover, the deficiencies of existing methods are researched and criticized, and a guide for future directions is provided.

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The data presented in this study are available on request from the corresponding author.

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Acknowledgments

We convey our sincere appreciation to Qingdao University of Technological and Machine Vision Laboratory. We also wish to acknowledge the younger martial brothers and sisters who provide help but do not appear in the ranks of the authors.

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Funding

This work was supported by a National Natural Science Foundation (Grant No. 62171247) and two Natural Science Foundation of Shandong Province (Grant No. ZR2020MF001 and Grant No. ZR2020QF101).

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  1. School of information and Control Engineering, Qingdao University of Technology, Qingdao, 266525, Shandong, China

    Yuguo Zhou, Yanbo Ren, Erya Xu, Shiliang Liu & Lijian Zhou

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  1. Yuguo Zhou
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Conceptualization: Zhou YG; Methodology: Ren YB, Zhou YG; Formal analysis and investigation: Zhou LJ; Writing - original draft preparation: Ren YB; Writing - review and editing: Zhou YG, Xu EY, Liu SL; Funding acquisition: Zhou YG; Resources: Zhou YG; Supervision: Zhou YG. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Lijian Zhou.

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Zhou, Y., Ren, Y., Xu, E. et al. Supervised semantic segmentation based on deep learning: a survey. Multimed Tools Appl 81, 29283–29304 (2022). https://doi.org/10.1007/s11042-022-12842-y

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