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Project: Robust symbol detection and recognition in piping and instrumentation diagrams


Project description 

This project is concerned with the recognition of symbols of piping and process equipment together with the instrumentation and control devices that appear on piping and instrumentation diagrams (P&ID). Each item on the P&ID is associated with a pipeline. Piping engineers often receive drawings from clients in PDF format rather than a digital format produced in engineering software packages. This is due to confidentiality reasons and can also be true when dealing with legacy drawings. 

From P&IDs, piping engineers are required to generate material take off (MTOs) documents which will include the list of equipment, their quantities, sizes, etc. The MTO is used to create estimates of costs. To generate MTOs, piping engineers will manually go through every single drawing, identify the symbols, count them, and fill in an MTO spreadsheet. It can take around 36 hours of a highly skilled engineer’s time to generate an MTO for 10 drawings and every project has around 500-1000 drawings. Additionally, the process is prone to mistakes and may require additional checks. Mistakes in quantities, sizes or materials can have huge cost implications. 

An AI based approach helps automate the generation of MTOs from P&IDs. It is expected that a human interaction level will still be required for instance in some cases engineers’ expertise may be required to decide on the quantities based on what is presented on drawings. Note that every project has its own legend sheets i.e. symbols differ between projects. 

The desired model needs to be able to: 

  1. Identify equipment and instruments on drawings with reasonable accuracy
  2. Process 100s of documents simultaneously
  3. Require reasonable training hours (as symbols are project specific) 
  4. Enable extraction of line numbers as well as symbol recognition
  5. Associate line numbers with symbols 
  6. Automatically populate an MTO spreadsheet 

Any scripts must be written in Python. 

Desired Skills

  • Computer vision 
  • Machine learning
  • Deep learning for object recognition
  • Python programming language
  • Text recognition 
  • Ability to apply research findings in a timely manner
  • Proactive in trying and testing new ideas and working with engineers

About McDermott 

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Contact info

Interested students should send their CVs directly to Ya-alimadad, Mina <>

Mykola Pechenizkiy
Secondary supervisor
Stiven Schwanz Dias
External location
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