Accurately identifying the location and type of internal defects in gas-insulated switchgear (GIS) is a challenge. To address this challenge

this study proposes a novel method for the nondestructive detection of GIS internal defects. This method is based on x-ray digital radiography (X-DR) technology and an improved real-time models for object detection (RTMdet) algorithm

namely GIS-specific localised internal defect-RTMdet. Firstly

the X-DR images of GIS are preprocessed by dynamic limit adaptive histogram equalisation algorithm to improve the images contrast. Then

a convolution shuffle upsample module for upsampling is proposed

which enlarges the defect feature map by multi-convolution and pixel shuffling

reduces the information loss

and enhances the interaction between the feature information. Finally

both the multi-channel attention net and the global attention mechanism are integrated into the neck network for enhancing local feature extraction and global information association. Experiments demonstrate that the proposed method achieves a mean average precision @0.5:0.95 of 94.9%

showcasing excellent overall performance and generalisation ability

and is more suitable for accurate nondestructive detection of internal defects of GIS in complex scenarios.