4.5 Article

A novel centerline extraction method for overlapping fish body length measurement in aquaculture images

Journal

AQUACULTURAL ENGINEERING
Volume 99, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.aquaeng.2022.102302

Keywords

Aquaculture information engineering; Fish length measurement; Centerline extraction; Overlapping identification

Funding

  1. National Key Research and Development Program of China
  2. National Natural Science Foundation of China
  3. LiaoNing Revitalization Talents Program
  4. Dalian Technology Talents Program
  5. Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture and Rural Affairs, P.R. China
  6. [2019YFD0900902]
  7. [51822901]
  8. [31972843]
  9. [31872610]
  10. [XLYC907139]
  11. [XLYC2007045]
  12. [2020RQ107]
  13. [2020RJ102]

Ask authors/readers for more resources

This study proposes a method to extract the centerlines of overlapping fish by processing images captured from a top-view perspective. The results demonstrate excellent performance in extracting fish centerline information, even for fish that overlapped in images captured from a top view.
Information on the length of cultured fish is crucial to evaluate their growth status and biomass. However, existing methods to estimate fish body length are based primarily on manual sampling, which is invasive, time-consuming, and labor-intensive. In contrast, such information can be acquired via noncontact methods relying on images of the animals' centerlines in an image. The centerline extraction method has been widely applied as an efficient approach. However, the greatest challenge in measuring the length of cultured fish is that of accurately extracting the animals' centerlines when fish overlap in an image. To address this problem, we propose a method to obtain the centerlines of overlapping fish by processing images captured from a top-view perspective. First, fish contours are segmented using convex-concave points. Second, the contour segmentations belonging to each fish are identified by an algorithm that matches heads to tails; their centerlines are thus extracted and optimized. We conducted both laboratory and on-site experiments, and results show that the proposed method exhibited excellent performance in extracting fish centerline information, even for fish that overlapped in images captured from a top view. Moreover, it also improved on the performance of existing methods in measuring the length of cultured fish from such images.

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