A Transmission Line External Force Damage Prevention System Integrating YOLO v8 Network and Intrusion Detection Algorithm

Nowadays, in addition to foreign object damage, there is also network security damage in the form of external force damage to transmission lines. Therefore, traditional methods are no longer suitable for the current external force damage prevention system of transmission lines. In response to such problems, the convolutional block attention module is used to improve the object detection algorithm, and an image detection model is proposed. A new transmission line external force damage prevention system integrating the object detection algorithm and intrusion detection algorithm is proposed by integrating the image detection model with the intrusion detection algorithm. The proposed image detection model achieved the highest classification accuracy of 95.45%, 89.78%, 90.02%, and 96.03% for images of bird nests, balloons, kites, and leaves, respectively. The classification accuracy of the transmission line external force damage prevention system for four network attack methods, including normal recording, denial of service attacks, illegal access from remote machines, and illegal access of local superuser privileges by ordinary users, reached the highest values of 97.36%, 95.23%, 96.31%, and 95.01%, respectively. Therefore, a new model combining two algorithms effectively improves external force damage prevention’s dynamic adaptability and accuracy for transmission lines, which provides an efficient and accurate solution.