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The detection of composite insulator defects in substations still relies on inspection by operators，which is a heavy workload and prone to leakage due to visual fatigue.To reduce the computational resource consumption and shorten the training time，the region convolutional neural networks（RCNN）was improved by reorganizing the convolution kernel，and a detection method was proposed for insulator crack shape features. The method can meet the premise of insufficient training sample data，but also can get better convolutional neural networks （CNN）training effect，and finally achieve accurate crack recognition. In the training phase，the RGB three-channel decomposition method was used to expand the training data set，the median filtering method was used to remove the noise，the improved convolutional kernel was used to train the CNN. In the test phase，the images were decomposed by RGB three-channel decomposition and input to CNN to get the exact crack center coordinates and length. The non-maximum suppression（NMS）algorithm was used to de-weight the images to get the final crack recognition results. The example analysis shows that the propose method 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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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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