Fan surface defect online detection quantification method based on unmanned aerial vehicle and deep learning

• Main Authors: YAN JIQUAN, ZHAO FENG, ZHANG TONGZHOU, ZHANG YAXUAN, FANG JIANHAO, TAN JIANRONG, HU WEIFEI, JIAO QING
• Format: Patent
• Language: Chinese; English
• Published: 14.06.2024
• Subjects: CALCULATING; COMPUTING; COUNTING; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; PHYSICS

The invention discloses a fan surface defect online detection and quantification method based on an unmanned aerial vehicle and deep learning, and the method comprises the steps: improving a receptive field fixing problem of a network through introducing a moving window self-attention mechanism, designing a lightweight image segmentation module based on a feature pyramid and a feature fusion module, and carrying out the further improvement of a network structure, according to the method, multiple tasks of detecting and segmenting defect features in a fan surface image are realized, and skeleton extraction is carried out on an output semantic feature map by utilizing convex hull fitting and a refining algorithm, so that defect length information and area information are acquired. The actual length of the defect is calculated according to the camera imaging principle, and finally defect damage evaluation is achieved through threshold segmentation. And a data communication link between the unmanned aerial vehicl