Transmission line defect detection based on feature fusion

Abstract

Transmission lines are the lifeblood of the power system, and regular line inspections are required to ensure the operation of transmission lines. However, due to the special high-voltage environment of transmission lines, the pictures collected by UAVs are characterized by many background interferences and small defect samples, and it is difficult to directly detect line inspection methods based on deep learning. For this reason, this paper proposes a high and low-dimensional fusion framework, which divides the target detection task into four modules, and greatly reduces the technical volume of the model while retaining the high-dimensional features through the collaboration between modules. This paper also introduces the window attention mechanism based on YOLOv7, which allows the model to focus on local information and improve the model’s detection ability on small targets and also introduces the Wise-IOU loss to improve the model’s prediction accuracy and inference time. Experiments prove that the method in this paper can significantly improve the accuracy of model prediction while meeting the speed requirements of industrial scenarios.