Abstract: To solve the worldwide problems in the traditional poultry meat cutting process, including high labor costs, high safety risks, and other global problems, a robot autonomous cutting production line system was designed by integrating the key technologies such as accurate perception, rapid cutting, and autonomous deboning. To meet the requirements of efficient automatic classification and packaging for chicken breasts and wings (including wing tip, middle joint, and root) produced in the autonomous cutting process, a new recognition method combining image processing, convolutional neural network (CNN) classification, and the hardware/software collaborative framework was proposed, aiming to achieve the function integration and real-time requirements of image acquisition, processing, and detection. Firstly, the meat area was extracted to distinguish chicken breast and wing tip; secondly, the wing middle and root were classified based on CNN technology; finally, the recognition speed via software/hardware cooperation was estimated by parameter and computational efficiency analysis in the recognition algorithm. With the meat identification system platform built, the motion blur of the conveyor belt at full speed was analyzed, and the data set was expanded by data enhancement. In order to reduce the amount of computation, only the image data of R channel was used as the input of neural network. The results show that the recognition accuracy of chicken breast and wing tip can reach 100%, and that of wing middle and root can reach 98.7%, with recognition speed of 0.047 seconds, which could meet the research and development needs of efficient sorting of 10,000 poultries per hour for future work. 

Key words:  , robot, sorting system, automated production line, convolution neural network, machine vision