SCAD-DETR: An Infrared Image Detection Method for Switchgear Equipment

With the rapid development of intelligent operation and maintenance of substations, higher requirements have been put forward for real-time monitoring of power equipment status. Infrared thermography, as a non-contact temperature detection means, has been widely used in the monitoring of power equipment. Based on this, this paper proposes an infrared image detection algorithm for switchgear power equipment (SCAD-DETR) based on the improved Real-Time Detection Transformer (RT-DETR) framework. First, we design a novel lightweight network architecture by combining the SMAFB (Synergistic Multi-Attention Transformer Block) and Convolutional Gated Linear Unit (CGLU) structures, which can effectively capture local and global information and model remote dependencies, thus improving the network’s ability to understand complex structures and understanding of long-distance information. Second, a multi-scale feature transformation self-attention module (MFTself Attention) is proposed, which can ensure efficient feature expression while retaining more spatial detail information, and significantly enhances the perception of regional information at the advanced feature level. Finally, we design a hybrid distillation strategy to finely optimize our model, aiming to improve the detection accuracy without increasing the computational cost. Experiments show that the proposed model achieves 99.5% mAP on switchgear equipment infrared data, which is a 5.6% improvement compared to the baseline model. In order to verify model generalizability, we further analyze and validate the model using publicly available datasets in this field, achieving 90% mAP and verifying its potential application in power equipment safety monitoring.