A knowledge-guided model of fitting detection in aerial transmission line images. (December 2020)

• Record Type: Journal Article
• Authors: Zhao, Zhenbing; Wu, Xueliang; Qi, Yincheng; Nie, Liqiang; Lv, Bin
• Is Part Of: Energy reports. Volume 6(2020)Supplement 9
• Page Start: 1071
• Page End: 1078
• Publication Date: 2020-12
• Subjects: Fitting detection -- Spatial attributes -- Co-occurrence model -- Graph -- Deep learning; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2020.11.075
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: 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.