ASSEMBLY MONITORING METHOD AND DEVICE BASED ON DEEP LEARNING, AND READABLE STORAGE MEDIUM

摘要

The present invention relates to an assembly monitoring method based on deep learning, comprising steps of: creating a training set for a physical assembly body, the training set comprising a depth image set D_i and a label image set L_i of a 3D assembly body at multiple monitoring angles, wherein i represents an assembly step, the depth image set D_i in the ith step corresponds to the label image set L_i in the ith step, and in label images in the label image set L_i, different parts of the 3D assembly body are rendered by different colors; training a deep learning network model by the training set; and obtaining, by the depth camera, a physical assembly body depth image C in a physical assembly scene, inputting the physical assembly body depth image C into the deep learning network model, and outputting a pixel segmentation image of the physical assembly body, in which different parts are represented by pixel colors to identify all the parts of the physical assembly body. In the present invention, parts in the assembly body can be identified, and the assembly steps, as well as the occurrence of assembly errors and the type of errors, can be monitored for the parts.