This invention relates to the field of defect
identification technology, and more particularly to an image positioning and
recognition system based on UAV inspection of wind
turbine blades. The
system includes an image positioning and recognition center, a
data acquisition module, a detection preparation module, a flight control module, an attitude
dynamic management module, a detection data module, a defect type identification module, a positioning
visualization module, and an operation display module. This invention uses image recognition and blade sway amplitude for dual
verification, initiating detection only when there is no
impact or only a slight
impact and the sway amplitude meets the standard, ensuring the rationality of the detection
initiation conditions. During detection, analysis is performed from two dimensions: hovering position and hovering attitude, avoiding abnormal detection data caused by UAV position deviation or attitude anomalies. After screening out effective defect areas, accurate defect type identification is achieved. Furthermore, through coordinate transformation, an intuitive 3D model of the defective blade is generated, providing precise guidance for maintenance work and significantly reducing maintenance
troubleshooting costs.