陆煜锌,赵云,肖勇,蔡梓文,王鹏.面向数字孪生低压配电网的基于Gabor-YOLO算法的架空线高效识别方法研究[J].电测与仪表,2023,60(3):40-46. Yuxin Lu,Yun Zhao,Yong Xiao,Ziwen Cai,Peng Wang.Research on efficient identification method of overhead lines based on Gabor-YOLO algorithm for digital twin low-voltage distribution network[J].Electrical Measurement & Instrumentation,2023,60(3):40-46.
面向数字孪生低压配电网的基于Gabor-YOLO算法的架空线高效识别方法研究
Research on efficient identification method of overhead lines based on Gabor-YOLO algorithm for digital twin low-voltage distribution network
Real-time acquisition and update of power line operating status from various measurement devices is the basis of the digital twin of low-voltage distribution networks. The primary task of obtaining power line operating status is to accurately identify power lines. This paper proposes an algorithm based on Gabor-YOLO for the efficient extraction of low-voltage overhead power lines, aiming at the problems of complex background, serious occlusion and weak target features in aerial images of low-voltage power distribution overhead lines. First, after preprocessing the image, such as grayscale and Gaussian filtering, the improved Gabor operator is used for feature extraction, and the foreground area is segmented in the image; secondly, in the improved YOLO network module, the power lines and auxiliary targets are located. And identify the final extracted power lines. The experimental results show that the improved Gabor operator can quickly extract the foreground area of the image, and the improved YOLO network can accurately extract the power lines in the foreground area. The experimental results show that the proposed method has the highest accuracy and extraction speed compared with other methods such as yolov4, and the mAP value can reach 93.6%, which meets the needs of practical work.
