GSLI‐RTMdet: An automatic nondestructive detection method for internal defects in gas‐insulated switchgear X‐DR images
Guote Liu, Zhenxing Su, Bing Luo, Yongxuan Zhu
Abstract
Abstract 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.