PID_Symbol_Detection

Installation and Setup

1. Create and Activate Virtual Environment

Windows

# Create virtual environment
python -m venv .venv

# Activate virtual environment
.venv\Scripts\activate

Linux/MacOS

# Create virtual environment
python3 -m venv .venv

# Activate virtual environment
source .venv/Scripts/activate

Usage Guide

Stage 1: Generic Symbol Detection (Class-Agnostic)

Data Preparation

python src/run_pipeline.py stage1 --prepare_data

Model Training

python src/run_pipeline.py stage1 --train_model

Complete Stage 1 Pipeline (Data Prep + Training)

python src/run_pipeline.py stage1

Stage 2: Symbol Classification (Few-Shot)

Data Preparation

python src/run_pipeline.py stage2 --prepare_data

Model Training

python src/run_pipeline.py stage2 --train_model

Complete Stage 2 Pipeline (Data Prep + Training)

python src/run_pipeline.py stage2

Inference

Stage 1 Inference

python src/run_pipeline.py stage1_inference

Stage 2 Inference (Label Transfer)

python src/run_pipeline.py stage2_inference

Evaluation

Stage 1 Evaluation

python src/run_inference.py evaluation --evaluate_stage1

Stage 2 Evaluation

python src/run_inference.py evaluation --evaluate_stage2

Complete Evaluation (Both Stages)

python src/run_inference.py evaluation

Data Organization

Directory Structure

data/
├── raw/
│   ├── images/          # Place your P&ID images here
│   └── labels_class_aware/  # Place corresponding ground truth labels here

Sample Dataset

The repository includes a sample dataset from the Dataset-P&ID collection, containing 5 P&ID images and their corresponding labels. This dataset is used for demonstration purposes and originates from the research paper available here.

Adding Custom Data

To use your own P&ID images:

1. Place your images in data/raw/images/
2. Place corresponding ground truth labels in data/raw/labels_class_aware/
3. Ensure labels follow the same format as the sample dataset
4. Change class_aware_class_names variable in config.yaml accordingly

Proposed Framework vs Conventional Framework

Benefits of Proposed Framework

Class-Agnostic Object Detection & One-shot Label Transfer is found to be more:

1. Generalizable to different underlying P&ID drawing styles
2. Robust to class-imbalance compared to equivalent class-aware counterparts.

Simplified Visual Walkthrough of Proposed Framework

1. Data preprocessing

This step breaks down large P&ID sheets into overlapping patches.

Plus, class-aware labels are transformed into class-agnostic to prepare for training a Yolo object detection model.

2. Train Yolo (Stage-1)

Trains a 'Generic' symbol detector

3. Inferencing with SAHI (Stage-1)

For large P&IDs infer on smaller patches and combine the results (implemented via SAHI ).

4. Label Transfer (Stage-2)

Train a model using one labeled image per symbol class (e.g. P&ID legend). The model can be a Siamese Network/ Prototypical (Zero-shot) Network or a Traditional classifier trained on augmented images.

If you use this package in your work, please cite it as:

@article{GUPTA2024105260,
title = {Semi-supervised symbol detection for piping and instrumentation drawings},
journal = {Automation in Construction},
volume = {159},
pages = {105260},
year = {2024},
issn = {0926-5805},
doi = {https://doi.org/10.1016/j.autcon.2023.105260},
url = {https://www.sciencedirect.com/science/article/pii/S0926580523005204},
author = {Mohit Gupta and Chialing Wei and Thomas Czerniawski},
}