Enhanced Symbol Recognition based on Advanced Data Augmentation for Engineering Diagrams

• Published in: International journal of advanced computer science & applications Vol. 13; no. 5
• Main Authors: Bin, Ong Kai, Hooi, Yew Kwang, Kadir, Said Jadid Abdul, Fujita, Haruhiro, Rosli, Luqman Hakim
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2022
• Subjects: Accuracy; Artificial neural networks; Computer science; Data augmentation; Datasets; Deep learning; Engineering drawings; Information science; Learning; Neural networks; Object recognition; Pattern recognition; R&D; Research & development; Statistical methods; Training
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2022.0130563

Symbol recognition has generated research interest for image analytics of engineering diagrams. Techniques including structural, syntactic, statistical, Convolution Neural Network (CNN) were studied to identify gaps of research. Despite popularity, CNN requires huge learning dataset, which often involves costly procurement. To address this, combination between CycleGAN and CNN is proposed. CycleGAN generates more learning dataset synthetically, thus yielding opportunity to improve accuracy of symbol recognition. In the domain of for engineering symbols, standard CNN model is developed and used in experimental testing. Different ratios of training dataset were tested in multiple experiments using Piping and Instrument Diagram (P&IDs) drawings. Result of highest accuracy for symbol recognition is up to 92.85% against baseline and other method. The results determined that gradual reduction of training samples, the effectiveness of recognition accuracy performance after using proposed method was remained substantially stable.