Automation of Material Takeoff using Computer Vision

Joy, John; Mounsef, Jinane

doi:10.1109/iaict52856.2021.9532514

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Faster R-CNN (Ren et al 2015) based methods were popular for engineering symbol detection (Ziran and Marinai 2018;Nguyen et al 2021;Gao et al 2020;Stinner et al 2021;Hu et al 2021;Joy and Mounsef 2021;Sarkar et al 2022;Jakubik et al 2022;Zheng et al 2022). Faster R-CNN is a two-stage object detector presented in 2015.…”

Section: Symbolsmentioning

confidence: 99%

“…Several engineering diagram studies required the use of a diagram legend (Joy and Mounsef 2021;Sarkar et al 2022). For example, Joy and Mounsef (2021) used a Faster R-CNN method with ResNet-50 for symbol detection in electrical engineering diagrams.…”

Section: Symbolsmentioning

confidence: 99%

“…An additional suggestion to reduce the annotation requirement is to include synthetic images in the training data. This was seen in the literature through various methods, including specialist engineering visualisation software (Kim et al 2021b) and image processing data augmentation techniques (Gao et al 2020;Joy and Mounsef 2021;Ziran and Marinai 2018;Jakubik et al 2022). Another method that has been explored is the use of deep learning generative models such as GAN-based approaches (Bin et al 2022;Elyan et al 2020a;Khallouli et al 2022).…”

Section: And Future Directionsmentioning

confidence: 99%

See 2 more Smart Citations

A review of deep learning methods for digitisation of complex documents and engineering diagrams

Jamieson,

Francisco Moreno-García,

Elyan

2024

Artif Intell Rev

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This paper presents a review of deep learning on engineering drawings and diagrams. These are typically complex diagrams, that contain a large number of different shapes, such as text annotations, symbols, and connectivity information (largely lines). Digitising these diagrams essentially means the automatic recognition of all these shapes. Initial digitisation methods were based on traditional approaches, which proved to be challenging as these methods rely heavily on hand-crafted features and heuristics. In the past five years, however, there has been a significant increase in the number of deep learning-based methods proposed for engineering diagram digitalisation. We present a comprehensive and critical evaluation of existing literature that has used deep learning-based methods to automatically process and analyse engineering drawings. Key aspects of the digitisation process such as symbol recognition, text extraction, and connectivity information detection, are presented and thoroughly discussed. The review is presented in the context of a wide range of applications across different industry sectors, such as Oil and Gas, Architectural, Mechanical sectors, amongst others. The paper also outlines several key challenges, namely the lack of datasets, data annotation, evaluation and class imbalance. Finally, the latest development in digitalising engineering drawings are summarised, conclusions are drawn, and future interesting research directions to accelerate research and development in this area are outlined.

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Section: Symbolsmentioning

confidence: 99%

Section: Symbolsmentioning

confidence: 99%

Section: And Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

A review of deep learning methods for digitisation of complex documents and engineering diagrams

Jamieson,

Francisco Moreno-García,

Elyan

2024

Artif Intell Rev

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Towards fully automated processing and analysis of construction diagrams: AI-powered symbol detection

Jamieson,

Moreno-Garcia,

Elyan

2024

IJDAR

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Construction drawings are frequently stored in undigitised formats and consequently, their analysis requires substantial manual effort. This is true for many crucial tasks, including material takeoff where the purpose is to obtain a list of the equipment and respective amounts required for a project. Engineering drawing digitisation has recently attracted increased attention, however construction drawings have received considerably less interest compared to other types. To address these issues, this paper presents a novel framework for the automatic processing of construction drawings. Extensive experiments were performed using two state-of-the-art deep learning models for object detection in challenging high-resolution drawings sourced from industry. The results show a significant reduction in the time required for drawing analysis. Promising performance was achieved for symbol detection across various classes, with a mean average precision of 79% for the YOLO-based method and 83% for the Faster R-CNN-based method. This framework enables the digital transformation of construction drawings, improving tasks such as material takeoff and many others.

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