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RISAT: real-time instance segmentation with adversarial training

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Abstract

With the development of artificial intelligence, autonomous driving has gradually attracted attentions from academia and industry. Detecting road conditions correctly and timely is essential to autonomous driving. Thus, we propose a flexible and parallel framework called RISAT for real-time instance segmentation. RISAT improves on YOLOv3 by adding a new parallel branch to generate masks. RISAT can produce a good performance on high-quality segmentation for each instance using GAN. Furthermore, we utilizes ROI class loss on both mask learning for each class and perceptual loss on detailed information. On the benchmark of MS COCO, the frame per second(FPS) of RISAT can achieve 43, which is much faster than that of MNC and FCIS. Besides, the average precision(AP) of RISAT is greater than the previous one-stage object detection method by 0.5.

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Acknowledgements

We would like to thank the anonymous reviewers for their invaluable comments.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Songwen Pei, Bo Ni & Zhenling Zhou

  2. State Key Lab of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Songwen Pei

  3. Shanghai Key Laboratory of Data Science, Fudan University, Shanghai, 200433, China

    Songwen Pei

  4. Department of Computer Science and Engineering, Santa Clara University, Santa Clara, 95053, CA, USA

    Tianma Shen

  5. College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

    Yewang Chen

  6. Department of Computer Science, Texas A & M University-Commerce, Commerce, TX, USA

    Meikang Qiu

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  1. Songwen Pei
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  4. Zhenling Zhou
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Corresponding author

Correspondence to Songwen Pei.

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Conflict of Interests

This work was partially funded by the National Natural Science Foundation of China under Grant (61975124), Shanghai Natural Science Foundation(20ZR1428600), the Open Project Program of Shanghai Key Laboratory of Data Science (NO.2020090600003), and the Open Project Funding from the State Key Lab of Computer Architecture, ICT, CAS under Grant CARCHA202111. Any opinions, findings and conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors.

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Pei, S., Ni, B., Shen, T. et al. RISAT: real-time instance segmentation with adversarial training. Multimed Tools Appl 82, 4063–4080 (2023). https://doi.org/10.1007/s11042-022-13447-1

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  • DOI: https://doi.org/10.1007/s11042-022-13447-1

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