Machine vision on the positioning accuracy evaluation of poultry viscera in the automatic evisceration robot system

Abstract

In the poultry slaughtering, accurate viscera positioning is essential to reduce the damage of internal organs. The introduction of machine vision technique can help to locate the viscera and can provide new direction for the poultry evisceration. After a midline abdominal incision of the poultry, the internal organs are taken out from the poultry that placed on the conveyor line by the parallel robot. Based on machine vision, the recognition accuracy of opened poultry viscera directly affects the level of visceral damage and residue caused by gripping manipulator. However, visceral positioning is often influenced by different noise in the abdominal cavity, such as mucous membranes and blood stains. Thus, the image segmentation of poultry viscera is a complex process, and it is challenging to remove the noise. In general, existing image segmentation methods can hardly segment visceral regions well. To strengthen the anti-noise ability, we proposed an improved regionbased active contour method with the level-set formulation. This method combined with several operations of color space conversion and top-low-hat transformation, which could extract the viscera contour and precisely removed the noise. The results showed that recognition accuracy of the heart-liver area and fat area in the viscera are 98.98% and 99.75%, respectively, while the overall viscera for poultry was 98.96%. The results of this experiment suggested that the proposed image segmentation algorithm could achieve the required accuracy for poultry viscera detection. Thus, the proposed visceral contour recognition method could be applied in poultry processing, providing critical information to guide the robot for automated evisceration.

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