Deep learning for symbols detection and classification in engineering drawings
| Autor: | Eyad Elyan, Laura Jamieson, Adamu Ali-Gombe |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Risk analysis
0209 industrial biotechnology Engineering drawing business.industry Computer science Cognitive Neuroscience Deep learning Diagram 02 engineering and technology Variation (game tree) Pattern Recognition Automated Task (project management) Deep Learning 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Artificial intelligence business Generative grammar |
| Zdroj: | Neural Networks. 129:91-102 |
| ISSN: | 0893-6080 |
| Popis: | Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practices such as inventory, assets management, risk analysis, and other types of applications. However, processing and analysing these drawings is a challenging task. A typical diagram often contains a large number of different types of symbols belonging to various classes and with very little variation among them. Another key challenge is the class-imbalance problem, where some types of symbols largely dominate the data while others are hardly represented in the dataset. In this paper, we propose methods to handle these two challenges. First, we propose an advanced bounding-box detection method for localising and recognising symbols in engineering diagrams. Our method is end-to-end with no user interaction. Thorough experiments on a large collection of diagrams from an industrial partner proved that our methods accurately recognise more than 94% of the symbols. Secondly, we present a method based on Deep Generative Adversarial Neural Network for handling class-imbalance. The proposed GAN model proved to be capable of learning from a small number of training examples. Experiment results showed that the proposed method greatly improved the classification of symbols in engineering drawings. |
| Databáze: | OpenAIRE |
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