Object Recognition Scheme for Digital Transformation in Marine Science and Engineering

Abstract

With the advancement of deep learning (DL), researchers and engineers in the marine industry are exploring the application of DL technologies to their specific applications. In general, the accuracy of inference using DL technologies is significantly dependent on the number of training datasets. Unfortunately, people in marine science and engineering environments are often reluctant to share their documents (i.e., P&ID) with third-party manufacturers or public clouds to protect their proprietary information. Despite this, the demand for object detection using DL technologies in image-formatted files (i.e., jpg, png, or pdf format) is steadily growing. In this paper, we propose a new mechanism, called a no-training object picker (NoOP), which efficiently recognizes all objects (e.g., lines, tags, and symbols) in image-formatted P&ID documents. Notably, it can recognize objects without any training dataset, thus reducing the time and effort required for training and collection of unpublished datasets. To clearly present the effectiveness of NoOP, we evaluated NoOP using a real P&ID document. As a result, we confirmed that all objects in the image-formatted P&ID file are successfully detected over a short time (only 7.11 s on average).

Keywords

• deep learning
• digital transformation
• piping and instrumentation diagram
• marine industry
• training datasets