Machine Vision Process Monitoring on a Poultry Processing Kill Line: Results from an Implementation

摘要

Researchers at the Georgia Tech Research Institute designed a vision inspection system for poultry kill line sorting with the potential for process control at various points throughout a processing facility. This system has been successfully operating in a plant for over two and a half years and has been shown to provide multiple benefits. With the introduction of HACCP-Based Inspection Models (HIMP), the opportunity for automated inspection systems to emerge as viable alternatives to human screening is promising. As more plants move to HIMP, these systems have the great potential for augmenting a processing facilities visual inspection process. This will help to maintain a more consistent and potentially higher throughput while helping the plant remain within the HIMP performance standards. In recent years, several vision systems have been designed to analyze the exterior of a chicken and are capable of identifying Food Safety 1 (FS1) type defects under HIMP regulatory specifications. This means that a reliable vision system can be used in a processing facility as a carcass sorter to automatically detect and divert product that is not suitable for further processing. This improves the evisceration line efficiency by creating a smaller set of features that human screeners are required to identify. This can reduce the required number of screeners or allow for faster processing line speeds. In addition to identifying FS1 category defects, the Georgia Tech vision system can also identify multiple "Other Consumer Protection" (OCP) category defects such as skin tears, bruises, broken wings, and cadavers. Monitoring this data in an almost real-time system allows the processing facility to address anomalies as soon as they occur. The Georgia Tech vision system can record minute-by-minute averages of the following defects: Septicemia Toxemia, cadaver, over-scald, bruises, skin tears, and broken wings. In addition to these defects, the system also records the length and width information of the entire chicken and different parts such as the breast, the legs, the wings, and the neck. The system also records average color and miss-hung birds, which can cause problems in further processing. Other relevant production information is also recorded including truck arrival and offloading times, catching crew and flock serviceman data, the grower, the breed of chicken, and the number of dead-on-arrival (DOA) birds per truck. Several interesting observations from the Georgia Tech vision system, which has been installed in a poultry processing plant for several years, are presented. Trend analysis has been performed on the performance of the catching crews and flock serviceman, and the results of the processed chicken as they relate to the bird dimensions and equipment settings in the plant. The results have allowed researchers and plant personnel to identify potential areas for improvement in the processing operation, which should result in improved efficiency and yield.