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RS-UNet: lightweight network with reflection suppression for floating objects segmentation

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Abstract

The research on image semantic segmentation of floating objects on water is beneficial to realize the automatic location of pollutants and facilitate the monitoring and salvage of pollutants. However, the segmentation accuracy of the existing deep neural networks is easily affected by the reflection noise of water surface. Besides, the models are not lightweight enough in practical applications. Based on the above two problems, the corresponding methods are proposed in this paper. Firstly, a Reflection Suppression Block (RSB) with Laplacian convolution is constructed to reduce the adverse impact of reflection on the task. Secondly, a Lightweight Encoder–Decoder (LED) is constructed to further improve the segmentation accuracy and reduce the number of model parameters. Finally, a novel model, named Reflection Suppression U-Net (RS-UNet), is formed by combining RSB and LED. The proposed model is trained with manually labeled floating objects segmentation dataset and achieves the best segmentation performance. The IoU of RS-UNet on the test set reaches 89.00%, which is 8.18% higher than that of U-Net, while its parameter number is only 20% of that of U-Net.

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Data availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by Jiangsu Petrochemical Process Key Equipment Digital Twin Technology Engineering Research Center Open Project (DTEC202103), Research and Development of Key Technologies of Smart Clothing Enterprise Management Cloud Platform (BY2022218).

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Authors and Affiliations

  1. School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, 213164, China

    Ning Li, Tingbo Zhang, Bo Li, Baohua Yuan & Shoukun Xu

  2. School of Computer and Information Engineering, Hohai University, Nanjing, 210098, China

    Ning Li

  3. Jiangsu Petrochemical Process Key Equipment Digital Twin Technology Engineering Research Center, Changzhou University, Changzhou, 213164, China

    Bo Li

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  1. Ning Li
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Correspondence to Shoukun Xu.

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Li, N., Zhang, T., Li, B. et al. RS-UNet: lightweight network with reflection suppression for floating objects segmentation. SIViP 17, 4319–4326 (2023). https://doi.org/10.1007/s11760-023-02664-2

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