面向机器人自主分割的肉品识别分类系统实现

针对传统禽类肉品分割环节存在的人工成本突出、卫生安全风险高等世界性难题,集成精准感知、快速切块、自主剔骨等关键技术,设计了面向机器人自主分割的全自动化生产线工艺流程.基于自主分块环节形成的鸡胸肉、翅尖、翅中、翅根后续高效自动化分类包装需求,提出了一种结合图像像素个数和卷积神经网络(CNN)分类的识别方法,建立软硬件协同...

Implementation of meat classification system for autonomous robotic cutting

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 byintegrating the key technologies such as accurate perception, rapid cutting, and autonomous deboning. To meet therequirements 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 imageprocessing, convolutional neural network (CNN) classification, and the hardware/software collaborative frameworkwas 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 wingmiddle and root were classified based on CNN technology; finally, the recognition speed via software/hardwarecooperation was estimated by parameter and computational efficiency analysis in the recognition algorithm. With themeat identification system platform built, the motion blur of the conveyor belt at full speed was analyzed, and the dataset 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 andwing 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.