Piping and Instrumentation Diagrams (P&IDs) are graphical representations utilized in chemical engineering plants. Due to confidentiality reasons and legacy drawings, these diagrams are sent in PDF format. Piping engineers need to make a material take-off (MTO), a document containing all the components of a P&ID from these drawings. Today, this is done manually, which proves to be time-consuming and laborious. A piping engineer spends approximately 36 hours per 10 P&IDs, with an average of 500-1000 P&IDs per project. Given the expertise and value of process engineers, this manual counting process incurs substantial costs and a repetitive workload. Consequently, there is a growing motivation to automate this process.

In response, this thesis introduces an innovative deep learning model, PandID-Net, designed specifically for P&IDs. PandID-Net uniquely integrates symbol detection, line detection, and text recognition into a single model, diverging from previous methods that relied on separate models and rule-based techniques. It is the first method that uses deep learning for the line detection task in P&IDs. This all in one approach not only simplifies the processing pipeline but also enhances computational efficiency in detecting and pinpointing symbols, lines, and text, as well as their interrelationships.

The optimal configuration of PandID-Net is found by an ablation study where the performance of individual components is tested in isolation. This optimized configuration is then evaluated and benchmarked against a prior study by Paliwal et al. on the same dataset. PandID-Net achieves a performance in F1 scores of 92.89 and 94.48 for line detection and keypoint detection respectively