Defect identification method for overhead transmission lines based on SIFT algorithm

Abstract

Maintaining high standards in wire installation for overhead transmission lines is vital for the dependability and safety of power systems. Traditional inspection techniques depend on manual evaluations, which are subjective and entail considerable safety hazards for workers. To tackle these issues, this paper suggests an automated wire defect detection approach utilizing image recognition, incorporated into an intelligent wire installation quality robot. The system uses a Scale-Invariant Feature Transform (SIFT) algorithm to precisely identify defect markers by initially extracting the texture features of standard wires and subsequently identifying variations that indicate faults. This approach improves defect detection by using optical imaging and real-time processing, ensuring resilience against differing environmental conditions. Tests conducted on various datasets demonstrated a missed detection rate of 4.2 %, a misjudgment rate of 3.5 %, and an overall detection accuracy of 92.3 %. These results substantiate the proposed method’s ability to enhance the automation and reliability of wire installation quality evaluation.