Object Detection for P&ID Images using various Deep Learning Techniques

Piping and Instrumentation Diagrams are detailed diagrams that show the piping and process equipment together with the instrumentation and control devices used in the process industry. These are updated during the plant life cycle to depict the latest changes and modifications done in the plant. These diagrams are detailed and are usually in the form of PDF, they are difficult to modify, and inferring details from these diagrams requires a deep understanding of plant process and engineering. To reduce the difficulty in finding and retrieving information from the diagrams, the proposal of automating the task of detection of the symbols and the texts embedded is explored in this paper. This paper compares various Deep Learning models on P&I Diagrams by using object detection for symbols using the Transfer Learning technique. Initially, the Connectionist Text Proposal Network algorithm is used for text detection in the images, then for the geometrically shaped objects, OpenCV library is used. The models trained by Transfer Learning are exported and the results are compared by performing symbol detection. This paper is an overall study of various deep learning methods implemented on P&I Diagrams.