An Improved Algorithm for Intelligent Identification of Distribution Network Equipment

With the continuous development of the distribution network, the distribution network equipment is gradually diversifying and becoming more complex. The inability of humans to correctly identify the type and model of distribution network equipment has led to frequent safety accidents. Many studies have used deep learning algorithms to identify distribution network equipment in the field, which can effectively reduce equipment mishandling in field operation and maintenance. In the process of real-world photography of distribution network equipment in field operations, there are problems such as complex backgrounds, many co-occurring object objects, different shooting distances and variable angles. The existing methods are difficult to effectively and accurately identify the target equipment. In response to the above problems, this paper proposes an improved algorithm model for intelligent identification of distribution network equipment. The model is based on a VGG16 recognition model with initialized weights to extract image features, combined with data sets for data enhancement operations such as cropping and flipping. At the same time, we introduce an attention mechanism into the model to make it more focused on specific regions in the image. Based on this, the model performs scene discrimination and device prediction tasks. The experimental results show that the model has a high accuracy rate and is practical for the identification of distribution network equipment in complex backgrounds.