Integrating deep learning-based object detection and optical character recognition for automatic extraction of link information from piping and instrumentation diagrams

Dong, F.*; Chen, S.*; Demachi, Kazuyuki*; Hashidate, Ryuta; Takaya, Shigeru

no journal, , 

Piping and Instrumentation Diagrams contain information about the piping and process equipment together with the instrumentation and control devices, which is essential to the design and management of Nuclear Power Plants. There are abundant complex objects on P&IDs, with imbalanced distribution of these objects and their linked information across different diagrams. Therefore, the content of P&IDs is generally extracted and analyzed manually, which is time consuming and error prone. To efficiently address these issues, we integrate state-of-the-art deep learning-based object detection and Optical Character Recognition models to automatically extract link information from P&IDs. Besides, we propose a novel image pre-processing approach using sliding windows to detect low resolution small objects. The performance of the proposed approach was experimentally evaluated, and the experimental results demonstrate it capable to extract link information from P&IDs of NPPs.