Research on product identification and positioning technology of machine vision based on Yolo detection framework

Machine vision is one of the core technologies for modern enterprises to develop unmanned, automated, and intelligent systems. First, conduct research on image acquisition and data expansion technology, carry out product identification analysis based on deep learning in order to further improve the unmanned and intelligent level of the production site, then perform product positioning based on the YOLO detection framework, and finally complete the application verification of product identification and positioning. Faced with the reality that the real-time requirements of product inspection on the production line are constantly increasing, the YOLO inspection framework is selected, and the model convolution structure and anchor points are added to meet the needs of product identification and positioning on the production line. The software environment and model construction are improved through the selection of light source, camera and lens, and finally the application practice of product identification and positioning is completed. Research and practice reveal that the experimental model can meet the real-time needs of intelligent manufacturing at a detection speed of more than 20 FPS under the premise of ensuring accuracy.