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Journal of Tropical Oceanography

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A Binocular Vision Method for In Situ Fish Size Measurement Integrating Instance Segmentation

Sun Qiantai1,2, Sun Liang1, Lei Pingshun1, Lin Xianzhi4, Chen Jianan1,Zhang Yurun1,2,Zhou Yan1,2,3, Liu Yuliang1,2, Liu Sheng4, Wang Xinwei1,2,3    

  1. 1 Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;

    2 School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;

    3 Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China;

    4South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China




  • Received:2026-04-27 Revised:2026-07-14 Accepted:2026-07-20
  • Supported by:

    Science & Technology Fundamental Resources Investigation Program (2022FY100605); National Key Research and Development Program of China (2024YFC2815203); the National Science Foundation of China (42276197)

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Abstract: To meet the demand for automated in situ fish size measurement in marine ecological monitoring, this study proposes a binocular stereo vision method incorporating instance segmentation priors, aiming to mitigate the interference of complex underwater environments with the accuracy of conventional stereo matching. The proposed method first employs the YOLOv8-Seg instance segmentation network to extract accurate fish masks, which are then used as spatial priors to guide the Semi-Global Block Matching algorithm. In this way, disparity computation is restricted to the target region, effectively suppressing background noise, reducing mismatches, and improving processing speed. The depth peak within the target region is subsequently extracted and analyzed, and the representative fish depth is determined using a disparity-frequency peak-based method. Finally, camera calibration parameters are incorporated to map the pixel-scale mask to physical space, enabling high-precision estimation of fish size. Based on this methodology, an underwater stereo camera system named “Shuijing-Trap” was developed. Controlled tank experiments showed that the proposed method achieved a mean relative error of 5.59% and a coefficient of variation of 6.68% in target size measurement, corresponding to centimeter-level accuracy. Moreover, owing to the introduction of segmentation priors, the measurement speed was improved by approximately three times, satisfying the requirements for real-time observation. In subsequent sea trials conducted in the Xisha Islands, the “Shuijing-Trap” system operated stably under varying depths and illumination conditions, and the measurement results showed high consistency, with a coefficient of variation of 3.34%. These results indicate that the proposed “segmentation prior + stereo matching” strategy achieves a favorable balance between measurement accuracy and efficiency, providing effective technical support for automated, digital, and fine-scale in situ monitoring of fish populations.

Key words: optical in situ monitoring, stereo vision, instance segmentation, fish size measurement, underwater 3D reconstruction