Squid is a significant aquatic product in China, yet its processing industry remains labor-intensive, leading to low efficiency and high labor intensity. This study introduces an image recognition-based soft-body modeling method to generate a 3D model of a suspended and inflated squid mantle from 2D images, providing a foundation for automated squid processing. The method uses OpenCV for image recognition to extract edge contours and geometric features of the squid mantle. MATLAB then refines these contours, deduces inner cavity contour points, and determines the mantle's rotational state. The contours are rotated around the central axis to construct a mesh model. This process accurately captures the shape of the squid mantle during processing, accounting for its deformable and flexible nature. Validation was performed using two datasets: different postures of the same squid body and five squid bodies of varying sizes. Results show that the maximum error in the model’s outer contour length compared to the actual body size is 0.66%, and geometric size errors across three postures are within 1%. This confirms the method's accuracy in generating 3D models from 2D images of squid bodies in various poses. The method ensures precise contour representation and is suitable for automated path planning in tasks like cleaning and cutting. Its non-contact nature makes it ideal for food processing, avoiding radiation issues associated with CT scanning. This study lays a foundation for automated squid processing, supporting tasks such as cleaning, cutting, and grading.
YU Yuelong, WEI Xiuting, MA Ze, LYU Xueya
. Research on 3D reconstruction of squid carcass based on image recognition [J]. Fishery Modernization, 2025
, 52(2)
: 109
.
DOI: 10.3969/j.issn.1007-9580.2025.02.012
