Advancing P&ID Digitization with YOLOv5

In the realm of engineering diagram recognition and digitization, significant progress is evident across various domains, including the handling of intricate diagram categories like Piping and Instrumentation Diagrams (P&IDs), scene text in images, and point symbols on scanned topographic maps. These advancements result from diverse methodologies, such as deep learning, neural networks, digital image processing, and the implementation of the YOLOv5 algorithm for symbol detection and recognition. These approaches substantially improve efficiency and automation in applications ranging from plant design to information extraction. They not only surmount challenges in processing complex visual data but also showcase how advanced technology like YOLOv5 is transforming industries, from manufacturing to geographic information systems. In the field of engineering diagram recognition and digitization, YOLOv5 achieves a 93% accuracy rate, demonstrating its significant impact. The model excelled in precision (92%), recall (94%), F1 score (93%), AUC score (0.97), nd ROC score (0.92), advancing document digitization and automation. These innovations pave the way for further enhancements in engineering and document digitization.