A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D Images

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2022-02-01

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Ülkü, İrem
Akagündüz, Erdem

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Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high-level and hierarchical image features; several deep learning-based 2D semantic segmentation approaches have been proposed within the last decade. In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. We started with an analysis of the public image sets and leaderboards for 2D semantic segmentation, with an overview of the techniques employed in performance evaluation. In examining the evolution of the field, we chronologically categorized the approaches into three main periods, namely pre-and early deep learning era, the fully convolutional era, and the post-FCN era. We technically analyzed the solutions put forward in terms of solving the fundamental problems of the field, such as fine-grained localization and scale invariance. Before drawing our conclusions, we present a table of methods from all mentioned eras, with a summary of each approach that explains their contribution to the field. We conclude the survey by discussing the current challenges of the field and to what extent they have been solved.

URI

https://hdl.handle.net/11511/96480

Journal

APPLIED ARTIFICIAL INTELLIGENCE

DOI

https://doi.org/10.1080/08839514.2022.2032924

Collections

Graduate School of Informatics, Article

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Citation Formats

İ. Ülkü and E. Akagündüz, “A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D Images,” APPLIED ARTIFICIAL INTELLIGENCE, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/96480.