OPEN ACCESS plSSN : 0374-8111 | elSSN : 2287-8815
OPEN ACCESS plSSN : 0374-8111elSSN : 2287-8815
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kfas, vol. 55, no. 5, pp.638-644, October, 2022

Current Status of Automatic Fish Measurement

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부경대학교 정보융합대학 스마트헬스케어학부 의공학전공

  • ABSTRACT

    The measurement of morphological features is essential in aquaculture, fish industry and the management of fishery resources. The measurement of fish requires a large investment of manpower and time. To save time and labor for fish measurement, automated and reliable measurement methods have been developed. Automation was achieved by applying computer vision and machine learning techniques. Recently, machine learning methods based on deep learning have been used for most automatic fish measurement studies. Here, we review the current status of automatic fish measurement with traditional computer vision methods and deep learning-based methods.

  • Keyword

    Fish measurement, Machine learning, Deep learning, Object detection, Segmentation