Method based on semantic segmentation for transmission line tree obstacle detection
CAI Wenbiao
WU Huaicheng
LI Lixue
DONG Yunpeng
ZHANG Jiayang
Abstract：To solve the problem of lower accuracy of transmission line tree obstacle detection and recognition in complex environment,a D-LinkNet model semantic segmentation technology based on convolution neural network was proposed.A coder-decoder structure was adopted by the algorithm.The structure took advan-tage of extended convolution to expand the receptive field and introduce feature extraction module.The net-work weight matrix was constructed by the correlation information matrix between pixels,for the improvement of network segmentation ability within the fuzzy boundary area.The simulation results show that the as-proposed algorithm improves the accuracy of tree obstacle detection to 97.87％,and the prediction accuracy is 12.23％higher than that of FCN model.The algorithm not only effectively improves the recognition accuracy,but also considers the operation speed,and has higher practical value.
Keywords：transmission linetree obstaclesemantic segmentationconvolution neural networkfeature recognitioncorrelation informationweight matrixfuzzy boundary area
Publication Date：2024-11-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 766-771 )