Structural and Computational Analysis of Segmentation Pipelines for Broiler Carcass Inspection Under Industrial Acquisition Conditions

The development of reliable computer vision systems for poultry slaughterhouses requires robust segmentation methods capable of handling challenging industrial conditions. This study evaluated the structural behavior, computational performance, and consistency of three segmentation pipelines for broiler chicken carcass inspection: (i) an edge-based approach using Canny detection and morphological operations, (ii) a threshold-based method using global binarization and contour extraction, and (iii) a deep learning-based approach for automatic background removal implemented with the rembg library. A dataset of 587 RGB images acquired in commercial slaughterhouses was analyzed. Segmentation performance was assessed using Intersection over Union, Dice coefficient, Overlap Error, Structural Similarity Index, Edge Preservation Index, and Fisher Discriminant Ratio, complemented by qualitative analyses of discordance maps and Sobel edge visualizations. Results showed that overlap-based metrics alone were insufficient, as high IoU and Dice values often concealed important boundary differences. Classical methods exhibited lower computational cost and processing times compatible with real-time applications but presented limitations in contour stability. The deep learning-based approach generated more continuous and structurally coherent boundaries, although at higher computational cost. These findings demonstrate that segmentation methods produce distinct structural representations that can directly affect the reliability of artificial intelligence systems for poultry inspection.