At present, there is a problem of insufficient utilization of regional features in the application of the existing knowledge graph based on GGNN in the defect classification of the bolt and nut pair of transmission lines. Therefore, a decision-making method of combining the bolt and nut pair with the original regional feature and the bolt and nut pair knowledge graph feature is proposed. For this reason, a method is proposed to combine the decision-making of the bolt and nut pair on the original regional features and the bolt and nut pair on the characteristics of the knowledge graph. First, the characteristics of the bolt and nut pair knowledge graph are combined with the adaptive normalization of the bolt and nut pair to the features of the original area. Then, the classification score vector based on fusion features and the classification score vector based on the bolt and nut pair to the original area feature are derived from the classifier respectively; Finally, the classification score vector of the fusion feature and the bolt and nut pair are fused to the classification score vector of the original region feature to obtain the final classification result. On this basis, this article uses bolt and nut pair to conduct multiple sets of defect classification experiments on the data set of the knowledge graph experiment. The experimental results show that the method of decision fusion of the bolt and nut pair to the original regional feature fusion the bolt and nut pair to the knowledge graph feature is better than the bolt and nut pair to the knowledge graph average precision, precision, and recall rate. It is effective Prove the improvement of the algorithm. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

At present, there is a problem of insufficient utilization of regional features in the application of the existing knowledge graph based on GGNN in the defect classification of the bolt and nut pair of transmission lines. Therefore, a decision-making method of combining the bolt and nut pair with the original regional feature and the bolt and nut pair knowledge graph feature is proposed. For this reason, a method is proposed to combine the decision-making of the bolt and nut pair on the original regional features and the bolt and nut pair on the characteristics of the knowledge graph. First, the characteristics of the bolt and nut pair knowledge graph are combined with the adaptive normalization of the bolt and nut pair to the features of the original area. Then, the classification score vector based on fusion features and the classification score vector based on the bolt and nut pair to the original area feature are derived from the classifier respectively; Finally, the classification score vector of the fusion feature and the bolt and nut pair are fused to the classification score vector of the original region feature to obtain the final classification result. On this basis, this article uses bolt and nut pair to conduct multiple sets of defect classification experiments on the data set of the knowledge graph experiment. The experimental results show that the method of decision fusion of the bolt and nut pair to the original regional feature fusion the bolt and nut pair to the knowledge graph feature is better than the bolt and nut pair to the knowledge graph average precision, precision, and recall rate. It is effective Prove the improvement of the algorithm. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Transmission lines are one of the most important infrastructures of energy Internet in China, and the effective detection of fittings is a necessary prerequisite to ensure their safety and stability. The fitting detection method based on classic deep learning only focuses on a single region and extracts the visual features of the region proposal into the classifier to identify the target, but never considers the connection between fittings. There is a certain regularity between fittings when constructing a complete and intact transmission line, interdependent and interact with each other. Therefore, we propose a knowledge-guided aerial transmission line image fitting detection model (KGFD) which uses two modules to learn the regularity of the fittings. The first module is an implicit module, starting from the spatial layout of the fittings on the image and taking the relative geometric features of the region proposal as the input knowledge of the spatial position of the fittings on the image. The second module is an explicit one. By introducing the prior knowledge of the fittings expressed in co-occurrence mode and using the region proposal as the node while the prior knowledge as the edge, a knowledge graph is constructed. The information spreads on the knowledge graph via a gated graph neural network, which realizes a combination of the prior knowledge of the fitting with the features of the region proposal. These two modules are added to the deep learning framework to detect the fitting targets, and the typical fitting data set of aerial transmission lines are used for testing. The AP value increased by 4.4% when IoU={0.5:0.95}and the AP value by 3.9% when IoU=0.5 compared with those of Faster R-CNN classification. (C) 2020 The Authors. Published by Elsevier Ltd.

Transmission lines are one of the most important infrastructures of energy Internet in China, and the effective detection of fittings is a necessary prerequisite to ensure their safety and stability. The fitting detection method based on classic deep learning only focuses on a single region and extracts the visual features of the region proposal into the classifier to identify the target, but never considers the connection between fittings. There is a certain regularity between fittings when constructing a complete and intact transmission line, interdependent and interact with each other. Therefore, we propose a knowledge-guided aerial transmission line image fitting detection model (KGFD) which uses two modules to learn the regularity of the fittings. The first module is an implicit module, starting from the spatial layout of the fittings on the image and taking the relative geometric features of the region proposal as the input knowledge of the spatial position of the fittings on the image. The second module is an explicit one. By introducing the prior knowledge of the fittings expressed in co-occurrence mode and using the region proposal as the node while the prior knowledge as the edge, a knowledge graph is constructed. The information spreads on the knowledge graph via a gated graph neural network, which realizes a combination of the prior knowledge of the fitting with the features of the region proposal. These two modules are added to the deep learning framework to detect the fitting targets, and the typical fitting data set of aerial transmission lines are used for testing. The AP value increased by 4.4% when IoU={0.5:0.95}and the AP value by 3.9% when IoU=0.5 compared with those of Faster R-CNN classification. (C) 2020 The Authors. Published by Elsevier Ltd.

Recent network research has demonstrated that the performance of convolutional neural networks can be improved by introducing a learning block that capture spatial correlations and channel-wise correlations. In this work, we propose a novel Channel-wise and Feature-points Reweights DenseNet (CAPR-DenseNet) architecture. The CAPR-DenseNet improves the representation power of the DenseNet by adaptively recalibrating the channel-wise feature responses and explicitly modeling the interdependencies between feature-points. First, in order to perform dynamic channel-wise feature recalibration, we construct the Channel-wise Feature Reweight DenseNet (CFR-DenseNet) by introducing the Squeeze-and-Excitation Module (SEM) to DenseNet. Then, we present a novel CAPR-DenseNet by adding a Feature-points Reweight Module (FPRM) to the CFR-DenseNet. Through massive experiments, we demonstrate that by recalibrating the channel-wise feature and the feature-points responses. Our CAPR-DenseNet performs better than DenseNet across challenging datasets CIFAR-10 and CIFAR-100.
© 2019 IEEE.

In the application of the deep learning to the unmanned aerial vehicle（UAV） autonomous inspection, a problem about the insufficiency of both quantity and quality for insulator images emerges. In the light of this situation, a sample expansion method based on a combination of statute and 3 D modelling technology is proposed. Its feasibility is verified in a deep convolutional neural network by using five kinds of insulator simulated samples. The classification accuracy acquired by the proposed method is higher verified by the comparison of the experimental results, which proves its superiority to the traditional method containing no simulated samples. It is concluded that the simulated intensive samples of pure background have a significant effect on the accuracy of network classification. And when the ratio of real samples to simulated intensive samples goes to an appropriate value, the classification has the best accuracy.