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Research on target detection method of underwater robot in low illumination environment

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Abstract

Underwater image detection remains a challenge due to problems such as noise, illumination inhomogeneity and low contrast. To solve these problems, this paper proposes a new level set segmentation model integrating saliency region detection (SDLSE). First, an underwater low-illumination saliency detection model is constructed and the target region is roughly segmented with the help of the saliency detection model to obtain pixel-level a prior shape information. Second, the a prior information is used as the shape constraint for finely segmenting the level set to improve the energy function of the level set. Based on the experimental data and fish dataset, the algorithm is statistically analyzed. It is verified that the segmentation effect of SDLSE model is better than other level sets in terms of segmentation accuracy and time efficiency.

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Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Acknowledgements

This research was funded by the City-School Cooperative Sailing Plan of Nanchong (Grant No. SXQHJH037).

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Authors and Affiliations

  1. Engineering Training Center, Southwest Petroleum University, Nanchong, China

    Chuan Ye, Youchun Xie, Qiyan Wang, Bo Pan & Chao Wang

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  1. Chuan Ye
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  2. Youchun Xie
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  3. Qiyan Wang
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  4. Bo Pan
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  5. Chao Wang
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Correspondence to Chuan Ye.

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Ye, C., Xie, Y., Wang, Q. et al. Research on target detection method of underwater robot in low illumination environment. Multimed Tools Appl 82, 26511–26525 (2023). https://doi.org/10.1007/s11042-023-14961-6

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  • DOI: https://doi.org/10.1007/s11042-023-14961-6

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