Research Area:  Machine Learning

Abstract:

With the rapid expansion of modern power grids, automated defect detection in high-voltage transmission lines has become a critical engineering challenge for preventing catastrophic failures and ensuring reliable electricity supply. While automated inspection has revolutionized power infrastructure maintenance, current vision-based methods still face three practical limitations in field applications: (1) susceptibility to complex background interference; (2) insufficient recognition accuracy for small-sized components; and (3) delayed response in real-time inspection scenarios. To address these industry pain points, this study develops a multi-scale fusion enhanced detection algorithm specifically optimized for power transmission line components. In response to these issues, this paper proposes an intelligent power transmission line defect detection algorithm based on multi-scale fusion, which introduce Coordinate Convolution, optimized decoupled detection head and improved loss function to solves the problems of low precision, poor robustness, and slow detection speeds faced by defect detection in power transmission network scenarios, laying a necessary theoretical foundation for subsequent practical applications.

Keywords:  

Author(s) Name:  Tianyi Wu , Liming Wang , Xiangyi Xu , Lei Su , Wenjing He , Xinting Wang

Publisher name:  oaepublish

DOI:  10.20517/ir.2025.24

Volume Information:  Volume: 5, (2025)

Paper Link:   https://www.oaepublish.com/articles/ir.2025.24