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Lightweight segmentation algorithm of feasible area and targets of unmanned surface cleaning vessels

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Abstract

To achieve real-time segmentation with accurate delineation for feasible areas and target recognition in Unmanned Surface Cleaning Vessel (USCV) image processing, a segmentation approach leveraging visual sensors on USCVs was developed. Initial data collection was executed with remote-controlled cleaning vessels, followed by data cleansing, image deduplication, and manual selection. This led to the creation of WaterSeg dataset, tailored for segmentation tasks in USCV contexts. Upon comparing various deep learning-driven semantic segmentation techniques, a novel, efficient Muti-Cascade Semantic Segmentation Network (MCSSNet) emerged. Comprehensive tests demonstrated that, relative to the state of the art, MCSSNet achieved an average accuracy of 90.64%, a segmentation speed of 44.55fps, and a 45% reduction in model parameters.

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Authors and Affiliations

  1. College of Marine Engineering, Jiangsu Ocean University, Lianyungang, 222005, China

    Jingfu Shen, Feiyue Liu & Chun Liu

  2. College of Mechanical Engineering, Jiangsu Ocean University, Lianyungang, 222005, China

    Yuanliang Zhang

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  1. Jingfu Shen
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  2. Yuanliang Zhang
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  3. Feiyue Liu
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  4. Chun Liu
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Contributions

1.Shen:Method innovation, paper writing, program writing； 2.Zhang:Paper review 3.Liu feiyue:draw pictures 4.Liu chun:make a table

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Correspondence to Yuanliang Zhang.

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Shen, J., Zhang, Y., Liu, F. et al. Lightweight segmentation algorithm of feasible area and targets of unmanned surface cleaning vessels. Machine Vision and Applications 35, 63 (2024). https://doi.org/10.1007/s00138-024-01537-z

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