PGF-Net: A Symmetric Cutting Path Generation and Fitting Optimization Method for Pig Carcasses Under Multi-Medium Interference

In the automated cutting process of pork carcasses, asymmetric cutting path planning is critical. However, various substances on the carcass surface, such as blood stains and fascia, severely interfere with the separation boundaries between fresh meat and bones, significantly reducing the accuracy of asymmetric cutting path planning. To address these issues, this paper proposes a method for generating and fitting optimized cutting paths for pork carcasses (PGF-Net). Specifically, this method comprises a cutting path generation module that integrates multi-scale boundary features and a cutting path fitting optimization module. The cutting path generation module extracts asymmetric boundary information by enhancing attention to boundaries across different regions, identifies key cutting points, and generates a coarse cutting path. The cutting path fitting optimization module then performs fitting optimization on the generated key cutting points to ultimately produce a refined asymmetric cutting path. Experimental results demonstrate that PGF-Net achieves mean root mean square errors of 0.4212 cm, 0.4651 cm, and 0.5313 cm across three cutting paths on six different pork carcass images. Findings confirm that this method enhances the yield of premium meat cuts while reducing tool wear costs. It provides an innovative technological solution for automated meat processing, holding significant industrial application value.