Raw meat 3D laser scanning imaging: Optimized by adaptive contour unit

Abstract

In the field of intelligent meat processing, the application of 3D laser scanning technology for identifying meat contours is essential for the accurate estimation of meat volume and quantitative slicing. In this study, based on 3D laser scanning technology, a contour adaptive shaping unit was developed to address the issue of scanning irregular meat contours and improve the scanning range and volume estimation accuracy. Moreover, we developed software for contour visualization imaging and volume estimation of raw meat using Halcon and Visual C# to optimize the scanning imaging and volume estimation of pork belly, hind shank, and pork loin. The results showed that the optimal shaping angles for chilled and frozen pork (loin, hind shank, and belly) were 60°, 30°, and 30°, respectively, with a scanning accuracy of ≥ 90 % and an average increase of 3 %. After shaping, the volume forms of the three types of raw meat remained stable for 12, 9, and 6 s in the chilled and frozen state. The corresponding coefficients of variation (CV) of imaging accuracy were 0.77 %, 1.16 %, and 0.54 %, respectively, with high stability and consistency of the imaging accuracy. In addition, the pork color and the transfer speed did not significantly affect the imaging performance of the adaptive shaping system (p > 0.05). The results demonstrated that the adaptive contour shaping system exhibited superior optimization capabilities in raw meat imaging, which provided the technical basis for the subsequent research and development pertaining to adaptive quantitative slicing devices for raw meat of various specifications.