Global perception algorithm for overhead line spacer based on improved YOLOv7 model

The overhead power line is located in an outdoor environment, and direct sunlight causes overexposure in the imaging results of the overhead line spacing rods, making it difficult to distinguish some detailed features on the surface of the spacing rods, resulting in a deviation in the perception of the status of the overhead line spacing rods. Therefore, a global sensing algorithm for overhead line spacers based on the improved YOLOv7 model is proposed. Introduce Retinex theory to enhance images. Introducing the YOLOv7 algorithm and improving the YOLOv7 algorithm model through spacer target box dimension clustering, convolution kernel optimization, and loss function optimization. Integrating the dimension of the spacer target box and the loss function into the improved YOLOv7 backbone network, neck network, and head network, the boundary box confirmation and regression process is used to design a global perception algorithm for overhead line spacers. The experimental results show that the overall convergence speed is relatively good, with an F1 value of up to 0.9, which can effectively perceive three types of perception results: normal, loose, and missing. The overall perception results of overhead line spacers have higher accuracy