Automated counting of piping and instrumentation diagram using artificial intelligence

Abstract

Accurately detecting and classifying symbolic representations is a significant hurdle in image processing, especially when dealing with complex engineering diagrams like Piping and Instrumentation Diagrams (P&IDs), which serve as crucial blueprints for industrial processes. However, the manual counting of equipment, instruments and lines within P&IDs remains a time-consuming task, impeding efficiency and accuracy in engineering projects. Traditional digitization methods, reliant on manual assessments are prone to errors and delays, especially when modifications occur. Proposed work presents an automated solution utilizing the Claude API to streamline the digitization process of P&IDs. By integrating both text reading and symbol recognition (object detection) capabilities. Our approach yields output in a tabular format, organizing the extracted data into distinct categories such as Equipment, Instruments, and Pipelines. It ensures accuracy by cross-referencing P&ID symbols from its database and verifying descriptions against equipment specifications or using its own model. It offers significant advantages over slower methods such as Sliding Window and R-CNN, providing a more efficient and accurate means of equipment and line counting. Through the integration of the Claude API, contractors and engineers can expedite the analysis of drawings, quantity estimates, and equipment lists, thereby enhancing productivity and minimizing errors in advanced engineering tasks.