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Abstract

A surface defect detection method for a primary cable of an aerostat based on few-shot learning includes the following steps. A hardware and software system environment is set. A surface image of the primary cable is acquired and processed to obtain augmented surface image data, which is labeled to construct a surface defect sample library. A defect detection network model is designed and constructed, and then is trained based on the surface defect sample library. A query set in the surface defect sample library is processed with the trained defect detection network model to obtain shallow texture features and high-level semantic features. The shallow texture features are transferred to the high-level semantic features through skip connection. The surface defect detection data under different detection operation modes are obtained at a terminal. This application also provides a surface defect detection system.

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