融合深度特征的输电线路金具缺陷因果分类方法

针对输电线路金具缺陷样本不足和缺陷目标形态多样化，仅仅利用深度学习模型导致金具缺陷分类准确率较低的问题，提出了一种结合深度网络和逻辑回归模型的因果分类方法。首先，通过样本扩充方法获得数量丰富化和角度多样化的数据集；然后，基于微调后的VGG16模型提取深度特征并进行特征处理，以构建符合因果关系学习的输入特征集；最后，通过全局混杂平衡进行金具缺陷特征与标签之间的因果关系学习，构建符合金具特点的因果逻辑回归模型，完成金具缺陷分类。为了证明所提方法的有效性，利用无人机实际采集的4类金具缺陷图片分别进行了实验，所使用的训练样本和测试样本数量较原始数据集提升了5倍左右。实验结果表明:所提方法可以实现对输电线路金具缺陷的精准分类，其中，防震锤相交和变形分类准确率分别达到了0.929 9和0.911 8，屏蔽环锈蚀和均压环损坏分类准确率分别达到了0.956 7和0.966 9。 

Causal classification method of transmission lines fitting defect combined with deep features

Aimed at the insufficient transmission lines fitting defect samples and diverse defect target shapes, a causal classification method combining deep network and logistic regression model is proposed to solve low defect classification accuracy when only using deep learning models. Firstly, rich and diverse datasets are obtained through the sample expansion method. Secondly, deep features are extracted based on the fine-tuned VGG16 model, and processed to construct an input feature set that conforms to causality learning. Finally, the causal relationship between fitting defect feature and label is learned through the global balance, and a causal logistic regression model is constructed to complete the classification of the fitting defects. Four types of fitting defect images collected by UAV are used respectively in the experiments to prove the effectiveness of the proposed method. The number of training and testing samples used is about 5 times higher than the original dataset. The experimental results show the proposed method can realize the accurate classification of the fitting defects, the classification accuracy of the shockproof hammer intersection and deformation reach 0.929 9 and 0.911 8 respectively, and the classification accuracy of the shielding ring corrosion and the grading ring damage reach 0.956 7 and 0.966 9 respectively.