By utilizing multiple forms of feature fusion, we are able to effectively eliminate incorrectly characterized defects. In this study, we propose a novel approach for distinguishing actual defects in wind turbine blades through the implementation of an efficient data processing method and a feature fusion module for identifying potential actual defects. Conventional image-based detection methods are also not capable of distinguishing between actual defects, such as coatings falling off, and false positives, such as dust, urine, or feces, in the abnormal areas of images. However, traditional visual inspection methods are often lacking in intelligence, have high rates of false detections, and are relatively inefficient. It is of paramount importance to conduct accurate inspections of wind turbine blades to identify and address any defects.
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