This invention provides a method and
system for online automatic wire layup defect detection using a
robot based on 3D vision. The method includes: constructing and calibrating a 3D vision imaging
system based on a Sham lens and a line
laser; synchronously moving with a wire layup
robotic arm to acquire two-dimensional images of the layup and reconstruct three-dimensional
point cloud data; fusing and analyzing the two-dimensional images and three-dimensional
point cloud data; using a lightweight
deep learning network to perform real-time defect detection on the two-dimensional images; simultaneously locating and segmenting defect regions based on
point cloud normal vectors and curvature information; fusing two-dimensional texture features and three-dimensional geometric features through a collaborative representation model to achieve accurate defect identification and location; transforming the defect information in a unified coordinate
system; and controlling multiple
laser projection devices to project onto the layup surface in real-time using a segmented
relay method. This invention achieves high-quality imaging under low
reflectivity, large
depth of field, and short-range conditions by fusing Sham lens imaging and line
laser scanning; achieves a recognition rate of over 95% in high-speed wire layup by combining dual-
modal fusion and lightweight few-sample learning; and utilizes multi-device collaborative calibration and
relay projection to achieve real-time defect feedback.