Semi-supervised symbol detection for piping and instrumentation drawings

Abstract

Current computer vision methods for symbol detection in piping and instrumentation diagrams (P&IDs) face limitations due to the manual data annotation resources they require. This paper introduces a versatile two-stage symbol detection pipeline that optimizes efficiency by (1) labeling only data samples with minimal cumulative informational redundancy, (2) restricting annotation to the minimal effective training dataset size, and (3) expanding the training dataset using pseudo-labels. In Stage-1, the method performs generic symbol detection, while Stage-2 focuses on symbol differentiation through metric learning. To enhance robustness and generalizability, the model is trained on a diverse dataset collected from both industry sources and web scraping. The achieved Top-1 accuracy is 85.39%, with a Top-5 accuracy of 95.19% on a test dataset containing 102 symbol classes. These results suggest the potential for a shift from resource-intensive supervised learning approaches to a more efficient semi-supervised paradigm.