An automatic evaluation method based on depth learning model was proposed to evaluate the insulator RTV spraying quality. This method first collects the insulator image sprayed with RTV and constructs a semantic segmentation model to extract the insulator RTV coating area from the image background. Then the extracted area is divided into rectangle blocks which will be classified into different types, including defected and undefected, through a neural network classification model. Finally, the fuzzy evaluation method is used to evaluate the RTV spraying quality according to the area proportion of the defect blocks in the whole image. Experiments show the proposed method is accurate and effective in that the evaluation results are consistent with the operation and inspection standards, which can meet the actual needs.
