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Abstract 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.
Jamieson et al. (Thu,) studied this question.