Wiring diagram detection and check based on deep learning and graph matching

The drawing and management of the traditional primary wiring diagram of plant and station mainly depend on the operators of power grid, which wastes time and labor and lacks scientific and verifiable reference standards. Towards this problem, based on the deep neural networks and digital image processing, we propose an algorithm for automatic detection, recognition and verification. Specifically, Faster R-CNN is first adopted to detect the electrical components of wiring diagram with 92% detection accuracy. In the meantime, an end-to-end text detection and recognition model is used to recognize the text with 94.2% detection accuracy and 92% character recognition accuracy. Then we take advantage of digital image processing technique to identify the connection of wiring diagram and topological relation. Finally, improved graph matching algorithm VF2 is used to check the difference between the electronic and manually maintained diagrams. The topological data is abstracted into an undirected graph, and the relative position information of components is obtained through the outline number. Based on the improved VF2 algorithm, we can compute the matching rate of two graphs to help the verification. Compared with the matching method of node traversal, the verification accuracy can be improved by 37.5%. Based on the first wiring diagram of some substations provided by a power supply company, this paper marks the electrical components of wiring diagram and contributes a small wiring diagram dataset. © 2021, Editorial Board of JBUAA. All right reserved.