Image Recognition and Processing Algorithm of Power Grid Facilities Map Based on Deep Learning

The traditional image processing method of power grid facilities map is based on iconography, which can alleviate the artificial pressure to a certain extent. However, due to the slow speed and low accuracy of the traditional iconography method, it is difficult to be applied in the field of fault inspection. In order to realize intelligent power inspection more quickly and accurately, an image recognition and processing algorithm of power grid facilities map based on deep learning is proposed to solve the problems of occlusion, inaccurate classification and insufficient feature extraction in the actually collected power grid facilities map images. The convolution operation module and residual module in YOLOv5 algorithm are improved, and the learning depth of the algorithm is deepened by increasing the number of convolution layers. At the same time, the SENet attention mechanism is added to the basic convolution module. The research results show that the accuracy of this model for power equipment identification has reached more than $99 \%$. And the recognition accuracy of fault defects can reach $\mathbf{9 2. 7 4 6} \%$. This model improves the detection accuracy and speed of power grid facilities map images, and also provides a novel and feasible scheme for intelligent detection of power grid facilities map images.