Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings.

The demand for digitisation of complex engineering drawings becomes increasingly important for the industry given the pressure to improve the efficiency and time effectiveness of operational processes. There have been numerous attempts to solve this problem, either by proposing a general form of document interpretation or by establishing an application dependant framework. Moreover, text/graphics segmentation has been presented as a particular form of addressing document digitisation problem, with the main aim of splitting text and graphics into different layers. Given the challenging characteristics of complex engineering drawings, this paper presents a novel sequential heuristics-based methodology which is aimed at localising and detecting the most representative symbols of the drawing. This implementation enables the subsequent application of a text/graphics segmentation method in a more effective form. The experimental framework is composed of two parts: first we show the performance of the symbol detection system and then we present an evaluation of three different state of the art text/graphic segmentation techniques to find text on the remaining image

A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams.

Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In this paper, we present a semi-automated framework which allows users to upload an undigitised Piping and Instrumentation Diagram, i.e. without any metadata, so that two key shapes, namely pipe specifications and connection points, can be localised using deep learning. Afterwards, a heuristic process is applied to obtain the text, orient it and read it with minimal error rates. Finally, a user interface allows the engineer to mark up the corrosion sections based on these findings. Experimental validation shows promising accuracy rates on finding the two shapes of interest and enhance the functionality of optical character recognition when reading the text of interest

Digital interpretation of sensor-equipment diagrams.

A sensor-equipment diagram is a type of engineering drawing used in the industrial practice that depicts the interconnectivity between a group of sensors and a portion of an Oil & Gas facility. The interpretation of these documents is not a straightforward task even for human experts. Some of the most common limitations are the large size of the drawing, a lack of standard in defining equipment symbols, and a complex and entangled representation of the connectors. This paper presents a system that, given a sensor-equipment diagram and a few impositions by the user, outputs a list with the reading of the content of the sensors and the equipment parts plus their interconnectivity. This work has been developed using open source Python modules and code, and its main purpose is to provide a tool which can help in the collection of labelled samples for a more robust artificial intelligence based solution in the near future

A Review of Neural Network Approach on Engineering Drawing Recognition and Future Directions

Engineering Drawing (ED) digitization is a crucial aspect of modern industrial processes, enabling efficient data management and facilitating automation. However, the accurate detection and recognition of ED elements pose significant challenges. This paper presents a comprehensive review of existing research on ED element detection and recognition, focusing on the role of neural networks in improving the analysis process. The study evaluates the performance of the YOLOv7 model in detecting ED elements through rigorous experimentation. The results indicate promising precision and recall rates of up to 87.6% and 74.4%, respectively, with a mean average precision (mAP) of 61.1% at IoU threshold 0.5. Despite these advancements, achieving 100% accuracy remains elusive due to factors such as symbol and text overlapping, limited dataset sizes, and variations in ED formats. Overcoming these challenges is vital to ensuring the reliability and practical applicability of ED digitization solutions. By comparing the YOLOv7 results with previous research, the study underscores the efficacy of neural network-based approaches in handling ED element detection tasks. However, further investigation is necessary to address the challenges above effectively. Future research directions include exploring ensemble methods to improve detection accuracy, fine-tuning model parameters to enhance performance, and incorporating domain adaptation techniques to adapt models to specific ED formats and domains. To enhance the real-world viability of ED digitization solutions, this work highlights the importance of conducting testing on diverse datasets representing different industries and applications. Additionally, fostering collaborations between academia and industry will enable the development of tailored solutions that meet specific industrial needs. Overall, this research contributes to understanding the challenges in ED digitization and paves the way for future advancements in this critical field

Using artificial intelligence to find design errors in the engineering drawings

Artificial intelligence is increasingly becoming important to businesses because many companies have realized the benefits of applying machine learning (ML) and deep learning (DL) in their operations. ML and DL have become attractive technologies for organizations looking to automate repetitive tasks to reduce manual work and free up resources for innovation. Unlike rule-based automation, typically used for standardized and predictable processes, machine learning, especially deep learning, can handle more complex tasks and learn over time, leading to greater accuracy and efficiency improvements. One of such promising applications is to use AI to reduce manual engineering work. This paper discusses a particular case within McDermott where the research team developed a DL model to do a quality check of complex blueprints. We describe the development and the final product of this case—AI-based software for the engineering, procurement, and construction (EPC) industry that helps to find the design mistakes buried inside very complex engineering drawings called piping and instrumentation diagrams (P&IDs). We also present a cost-benefit analysis and potential scale-up of the developed software. Our goal is to share the successful experience of AI-based product development that can substantially reduce the engineering hours and, therefore, reduce the project\u27s overall costs. The developed solution can also be potentially applied to other EPC companies doing a similar design for complex installations with high safety standards like oil and gas or petrochemical plants because the design errors it captures are common within this industry. It also could motivate practitioners and researchers to create similar products for the various fields within engineering industry

A review of neural network approach on engineering drawing recognition and future directions

Engineering Drawing (ED) digitization is a crucial aspect of modern industrial processes, enabling efficient data management and facilitating automation. However, the accurate detection and recognition of ED elements pose significant challenges. This paper presents a comprehensive review of existing research on ED element detection and recognition, focusing on the role of neural networks in improving the analysis process. The study evaluates the performance of the YOLOv7 model in detecting ED elements through rigorous experimentation. The results indicate promising precision and recall rates of up to 87.6% and 74.4%, respectively, with a mean average precision (mAP) of 61.1% at IoU threshold 0.5. Despite these advancements, achieving 100% accuracy remains elusive due to factors such as symbol and text overlapping, limited dataset sizes, and variations in ED formats. Overcoming these challenges is vital to ensuring the reliability and practical applicability of ED digitization solutions. By comparing the YOLOv7 results with previous research, the study underscores the efficacy of neural network-based approaches in handling ED element detection tasks. However, further investigation is necessary to address the challenges above effectively. Future research directions include exploring ensemble methods to improve detection accuracy, fine-tuning model parameters to enhance performance, and incorporating domain adaptation techniques to adapt models to specific ED formats and domains. To enhance the real-world viability of ED digitization solutions, this work highlights the importance of conducting testing on diverse datasets representing different industries and applications. Additionally, fostering collaborations between academia and industry will enable the development of tailored solutions that meet specific industrial needs. Overall, this research contributes to understanding the challenges in ED digitization and paves the way for future advancements in this critical field