Title: An interference recognition algorithm for auditing engineering drawings based on optimised CNN

Authors: Jing Zhang; Jie Li; Ailv Zhu; Zining Li

Addresses: State Grid Gansu Electric Power Research Institute, Lanzhou, 730070, China ' State Grid Gansu Electric Power Research Institute, Lanzhou, 730070, China ' State Grid Gansu Electric Power Research Institute, Lanzhou, 730070, China ' State Grid Gansu Electric Power Research Institute, Lanzhou, 730070, China

Abstract: Engineering drawing is one of the main objects of audit work. At present, the use of computer automatic identification technology to digitise engineering drawings has become the main method to solve the low efficiency of traditional audit work. However, few reports on solutions for sample attacks based on engineering drawings. An interference recognition algorithm for auditing engineering drawings based on optimised convolutional neural networks (CNN) is proposed in this paper. The purpose is to be able to proactively identify whether the engineering drawings contain adversarial samples before carrying out the audit of engineering drawings. The specific methods are as follows: ten types of images that may be embedded in engineering drawings are considered firstly; then, the LeNet and VGG networks were improved for these ten types of images, respectively. And the network model with high recognition accuracy against sample images is trained and generated. Finally, compared with the other three networks, the experimental results show that the accuracy of IVGG is improved on average 4.44% than the other three methods.

Keywords: engineering drawing; interference; active recognition; convolutional neural networks.

DOI: 10.1504/IJICT.2023.129953

International Journal of Information and Communication Technology, 2023 Vol.22 No.3, pp.254 - 266

Received: 07 Jan 2021
Accepted: 13 Apr 2021
Published online: 03 Apr 2023 *

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