Infrared thermal imaging

with such advantages as no contact and no damage

is one of the methods to monitor and diagnose the thermal defect of high voltage switchgear. The processing of massive infrared image data has put forward higher requirement for information mining

target recognition and intelligent diagnosis. However

the existing methods are still inadequate in the aspects of fault area identification

key feature extraction and defect classification. Therefore

a kind of intelligent diagnosis method for thermal defects of air insulated switchgear based on infrared image instance segmentation is proposed in this paper. The Mask R-CNN is used for automatic extraction and segmentation of regions of interest

a lightweight convolutional neural network is constructed and brought to the last step of Mask R-CNN and model training is performed by transfer learning so to achieve automatic identification of thermal defects. The test results show that with this method the average accuracy of the recognized regions of interest is 0.813 and the number of frames per second is 5.196. The accuracy and calculation speed are higher than those of current mainstream target detection methods. Meanwhile

the lightweight convolutional neural network introduced in the Mask R-CNN keeps classification accuracy reach 96.66% and is especially suitable for the intelligent terminals in the power internet of things.