Multimodal Fusion Network for Power Tower Semantic Segmentation and Inclination Detection

As the most fundamental supporting infrastructure of the distribution network, power towers require regular checks of their tilting status to ensure the system’s smooth operation. To overcome distinguishing objects in large-scale scenes using solely images or point clouds poses significant difficulties, we propose a multimodal fusion semantic segmentation network (MFSS) for power tower semantic segmentation and inclination detection. First, effective near-ground filtering and fixed-area slicing algorithms are proposed to address the issues of sample imbalance and insufficient data. Second, MFSS integrates RGB information and point cloud features to enhance the descriptive ability of the tower. Finally, a novel inclination detection method for distribution towers is proposed, estimating tower tilt from the axis between top and bottom centroids to improve accuracy and stability. Experimental results on our constructed dataset show that the proposed method outperforms existing algorithms, achieving 77.3% IoU and 96.69% per-class accuracy in tower segmentation. The mean angle deviation for tilt detection is 0.78$^{\circ }$, with a state judgment false rate of just 2.4%.