Introduction to Target Instance Segmentation Method Based on Deep Learning

Peisen Liu

doi:10.54254/2755-2721/2025.AST26037

Applied and Computational EngineeringOpen access

Introduction to Target Instance Segmentation Method Based on Deep Learning

Research Article

Open Access

Introduction to Target Instance Segmentation Method Based on Deep Learning

Peisen Liu ^1*

¹ School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China, 730000

^*Corresponding author: 2028162790@qq.com

Published on 13 August 2025

ACE Vol.175

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-237-9

ISBN (Online): 978-1-80590-238-6

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Abstract

With the rapid development of high-tech technologies in the 21st century, society is increasingly moving toward intelligentization. Intelligentization refers to endowing machines with human-like capabilities, and among these, the foremost is the ability to perceive the external environment—what is known as computer vision. Possessing perceptual capabilities means being able to distinguish and categorize elements within an environment, identifying those with similar attributes while differentiating those with distinct attributes. This involves organizing objects in the environment into cohesive units under unified concepts, enabling recognition of diverse entities. This process requires target instance segmentation methods based on deep learning. This paper primarily summarizes and analyzes early and mainstream approaches to target instance segmentation using deep learning, thoroughly examining existing research to guide future work in this field. Through analysis, this paper finds that diverse innovative strategies, such as parallel mask assembly (YOLACT), instance classification via spatial grids (SOLO), polar coordinate representation (PolarMask), and unified mask classification (MaskFormer), can effectively combine instance distinction with pixel-level classification.

Keywords:

Target Instance Segmentation, Convolution, Two-Stage, Single-Stage, Transformer

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References

[1]. Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.

[2]. O Pinheiro, Pedro O., Ronan Collobert, and Piotr Dollár. "Learning to segment object candidates." Advances in neural information processing systems 28 (2015).

[3]. Cai, Zhaowei, and Nuno Vasconcelos. "Cascade r-cnn: Delving into high quality object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.

[4]. Bolya, Daniel, et al. "Yolact: Real-time instance segmentation." Proceedings of the IEEE/CVF international conference on computer vision. 2019.

[5]. Wang, Xinlong, et al. "Solo: Segmenting objects by locations." European conference on computer vision. Cham: Springer International Publishing, 2020.

[6]. Xie, Enze, et al. "Polarmask: Single shot instance segmentation with polar representation." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.

[7]. Carion, Nicolas, et al. "End-to-end object detection with transformers." European conference on computer vision. Cham: Springer International Publishing, 2020.

[8]. Cheng, Bowen, Alex Schwing, and Alexander Kirillov. "Per-pixel classification is not all you need for semantic segmentation." Advances in neural information processing systems 34 (2021): 17864-17875.

References

[1]. Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.

[2]. O Pinheiro, Pedro O., Ronan Collobert, and Piotr Dollár. "Learning to segment object candidates." Advances in neural information processing systems 28 (2015).

[3]. Cai, Zhaowei, and Nuno Vasconcelos. "Cascade r-cnn: Delving into high quality object detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.

[4]. Bolya, Daniel, et al. "Yolact: Real-time instance segmentation." Proceedings of the IEEE/CVF international conference on computer vision. 2019.

[5]. Wang, Xinlong, et al. "Solo: Segmenting objects by locations." European conference on computer vision. Cham: Springer International Publishing, 2020.

[6]. Xie, Enze, et al. "Polarmask: Single shot instance segmentation with polar representation." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.

[7]. Carion, Nicolas, et al. "End-to-end object detection with transformers." European conference on computer vision. Cham: Springer International Publishing, 2020.

Cite this article

Liu,P. (2025). Introduction to Target Instance Segmentation Method Based on Deep Learning. Applied and Computational Engineering,175,65-70.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Application of Machine Learning in Engineering

ISBN: 978-1-80590-237-9(Print) / 978-1-80590-238-6(Online)

Editor: Marwan Omar, Mian Umer Shafiq

Conference website: https://www.confcds.org

Conference date: 19 August 2025

Series: Applied and Computational Engineering

Volume number: Vol.175

ISSN: 2755-2721(Print) / 2755-273X(Online)

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