Innovative segmentation technique for aerial power lines via amplitude stretching transform

作     者：Xu, Pengfei Sulaiman, Nor Anis Asma Ding, Yafei Zhao, Jiangwei 

作者机构：Pingdingshan Univ Dept Informat Engn Henan Int Joint Lab Machine Vis & Intelligent Syst Pingdingshan 467000 Henan Peoples R China SEGI Univ Fac Engn Built Environm & Informat Technol Ctr Sustainable Software Engn Kota Damansara Malaysia 

出 版 物：《SCIENTIFIC REPORTS》 (Sci. Rep.)

年 卷 期：2025年第15卷第1期

页      面：1-15页

核心收录：

基　　金：Henan Provincial Science and Technology Tackling Program [232102220098, 242102210131, 222102310601] Key Scientific Research Projects of Henan Higher Education Institutions [25B520031] 

主　　题：Pure amplitude stretching transform Power line image segmentation RTV transform 

摘      要：Accurate segmentation of power line targets helps quickly locate faults, evaluate line conditions, and provides key image data support and analysis for the safe and stable operation of the power *** aerial power line in segmentation due to the target is small, and the imaging reflected energy is weak, so the Unmanned Aerial Vehicle (UAV) aerial power line image is very susceptible to the interference of the environment line elements and noise, resulting in the detection of the power line target in the image of the defective, intermittent, straight line interferences and other low accuracy and real-time efficiency is not high. For this reason, this paper designs a pure amplitude stretching kernel function to form a Fourier amplitude vector field and uses this amplitude vector field to implement the stretching transformation of the amplitude field of the aerial power line image, so that the angular field after the Fourier inverse transformation can better react to the spatial domain line targets, and finally, after the Relative Total Variation (RTV) processing, the power line can be well detected. The proposed algorithm is compared with the main power line segmentation algorithms, such as Region Convolutional Neural Networks(R-CNN) and Phase Stretch Transform(PST). The average values of evaluation indicators PPA, MMPA and MMIoU of the image segmentation results of the proposed algorithm reach 0.96, 0.96 and 0.95 respectively, and the average time lag of detection is less than 0.2s, indicating that the accuracy and real-time performance of the segmentation results of the proposed algorithm are significantly better than those of the above algorithms.