基于语义分割与证据理论的电杆倾斜检测及风险评估方法

A pole tilt detection and risk assessment method based on semantic segmentation and evidence theory

随着无人机成为电力巡检的重要方式,基于机器视觉技术的无人机图像分析方法可为电杆倾斜检测及风险评估提供辅助手段。提出一种基于语义分割与证据理论的电杆倾斜检测及风险评估方法。基于U-Net和连通区域标记算法的电杆图像分割方法,对图像按照语义类别进行逐像素分类,并利用连通区域标记算法消除非电杆区域。在构建电杆倾斜判据的基础上,提出基于证据理论的电杆倾斜检测方法,利用合成规则将多个判据的结论融合。给出电杆倾斜风险评估方法,并将倾斜等级作为评估结果。实验结果表明所提方法可提高检测的准确性。

Now that UAVs(unmanned aerial vehicles)have been widely used for power inspection,the UAV image analysis method based on computer vision technology can provide auxiliary means for pole tilt detection and risk as⁃sessment.This paper proposes a method of pole tilt detection and risk assessment based on semantic segmentation and evidence theory.The pole image segmentation method based on U-Net and connected component labeling algo⁃rithm is used to classify the images pixel by pixel according to semantic categories,and non-pole regions are elimi⁃nated by using the connected component labeling algorithm.By the construction of pole tilt criterion,a method for pole tilt detection based on evidence theory is proposed to fuse the conclusions of multiple criteria by using synthetic rules.The tilt risk assessment method is given,and the tilt level is taken as the assessment result.The experimental results show that the proposed method can improve detection accuracy.