Our solution achieved a 93% accuracy rate, far exceeding the previous model’s 70% accuracy.
Meet our client
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Client’s Challenge
The major player in the chemical industry needed to digitize complex P&ID diagrams to create a Digital Twin of their facility. This required recognizing and classifying over 150 technical symbols in low-quality, cluttered diagrams. Manual digitization was slow, labor-intensive, and error-prone, delaying the process.
Our Solution
We developed a custom fully-convolutional, multi-scale neural network designed for P&ID digitalization. The system accurately detected small, densely packed symbols and mapped their connections. By integrating deep neural networks, graph-based algorithms, machine learning models, and heuristics, we tackled the complexity of the diagrams. With over 180,000 labeled objects, the model was trained on high-quality data to ensure precision.
Client’s Benefits
Our solution achieved a 93% accuracy rate, far exceeding the previous model’s 70% accuracy. This significantly reduced the manual effort required for P&ID digitization.
