On P&ID Symbol Recognition using Transformers - Cunha, Leonardo K. T. da; Leite, Eduardo F.; Morais, Lucas Eduardo C.; Harrison, Alessandra; Valiati, João F. (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região SulDO - 10.5753/eramiars.2025.16628, 2025)
Machine-interpretable graphs of P&IDs are essential to advance automation and digital twins; however, most diagrams remain in vector formats like DWG, limiting automated use. Conversion is further hindered by symbol variability, scarce annotations, and the lack of openly available datasets. This work addresses these challenges by evaluating transformer-based detection of P&ID symbols with DETR, comparing backbones pretrained on ImageNet, COCO, and a domain-specific synthetic data. The study analyzes their generalization from synthetic to real diagrams, with evaluations on real world datasets (OPEN100, IPD) showing that domain-specific pretraining consistently improves performance of about 1 mAP point, underscoring its value for robust industrial diagram interpretation.