An Electrical Equipment Identification Method Based Attention Mechanism for Infrared Image

Infrared thermal image technology is widely used for detecting the state of electrical equipment due to its noncontact advantages. However, as the scale of electrical equipment continues to expand, the analysis of infrared images, characterized by big data, still relies on manual methods, resulting in low efficiency and high costs. Therefore, this paper proposes an infrared image identification method for electrical equipment based on a deep convolution neural network. First, infrared images of electrical equipment, such as insulators, transformers, and circuit breakers, are collected, and dataset augmentation techniques like cropping and rotation are applied. A deep convolutional neural network model is then established by incorporating a multi-scale approach, with training conducted using cross-entropy loss functions. Finally, a coordinated attention mechanism based on regions of interest is introduced to enhance identification accuracy. The results demonstrate that the proposed method achieves a mean Average Precision (mAP) of $\mathbf{9 3. 6 4 \%}$ in validation, with the coordinated attention mechanism improving the mAP by $4.24 \%$. Thus, the proposed method significantly enhances identification accuracy and lays a foundation for the condition monitoring of electrical equipment.