The detection of composite insulator defects in substations still relies on manual inspection. In this paper, we propose a detection method for insulator crack shape features by improving the RCNN convolution kernel. The method can meet the premise of insufficient training sample data, but also can get better CNN training effect, and finally achieve accurate crack recognition. In the training phase, the RGB three-channel decomposition method is used to expand the training data set; the median filtering method is used to remove the noise; the improved convolutional kernel is used to train the CNN; in the test phase, the images are decomposed by RGB three-channel decomposition and input to CNN to get the exact crack center coordinates and length; the NMS algorithm is used to de-weight the images to get the final crack recognition results. The example analysis shows that the method in this paper can still achieve good recognition accuracy and accurately identify the specific location of cracks under the premise of insufficient training samples.
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