基于双视卷积神经网络的输电线路自动巡检

Research on automatic inspection of transmission line based on cross-view convolution neural network

卷积神经网络算法被广泛应用于输电线路自动巡检领域,但传统卷积神经网络电力缺陷识别模型的泛化能力较差。为此,提出一种融合双角度图像信息的卷积神经网络检测算法(CVR-RCNN),其利用2个视角可见光图像识别输电线路的常见缺陷。经实验测试,CVR-RCNN模型具有良好的鲁棒性,受试者工作特性(ROC)曲线下面积(AUC)值高达0.927,缺陷检测准确度较传统算法有显著提高。因此,CVR-RCNN能明显改善电力缺陷检测效果,可为无人机自动巡检输电线路提高准确稳定的算法架构。

The convolutional neural network algorithm is widely applied in the automatic inspection of transmission lines.However,the generalization ability of traditional convolutional neural network power defect-recognition model is not ideal.Under the background,this paper proposes a cross-view relation region convolutional neural network(CVR-RCNN)detection algorithm that integrates dual-angle image information,which utilizes two-view visible light images to identify typical defects in transmission lines.The testing shows that the CVR-RCNN model has good robustness.The area under curve(AUC)value of the receiver operating characteristic(ROC)curve is as high as 0.927,and the defect detection accuracy is significantly improved compared with traditional algorithms.Therefore,CVR-RCNN can significantly improve power defect detection and improve the accuracy and stability of the algorithm architecture for the automatic inspection of transmission lines by UAVs.