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Research Article | Open Access | Online First

A YOLO-based intelligent detection algorithm for risk assessment of construction sites

Ruiyang Feng, Yu Miao, Junxing Zheng( )
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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Abstract

Construction safety accidents have become increasingly frequent in recent years, leading to numerous casualties and substantial property losses. These incidents are often attributed to inadequate supervision on construction sites and workers’ low safety awareness. Traditional manual management methods, which are labor-intensive and resource-consuming, are no longer effective. Therefore, this study proposes a novel single-stage model based on YOLOv8s, designed for two primary purposes: detecting workers’ personal protective equipment and monitoring and recognizing when workers enter hazardous areas. The model provides real-time feedback on detection results to reduce the incidence of construction accidents. Additionally, a brief design for distance calculation was introduced. The model was trained for 200 iterations on a Roboflow dataset comprising 103,500 annotated images. Experimental results showed that YOLOv8s outperformed YOLOv8n, YOLOv5s, and YOLOv5n in detection performance, achieving a mean average precision with the intersection over union (IoU) threshold set to 50% (mAP50) of 84.0%, precision of 85.0%, and recall of 60.5% across 9 detection classes. By leveraging artificial intelligence technology, this study aims to offer an effective method for enhancing construction site safety, which can be further improved with additional images and a more robust network architecture.

Keywords

deep learningobject detectionconstruction safetyYOLOv8

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Journal of Intelligent Construction
DOI: 10.26599/JIC.2024.9180037
Cite this article:
Feng R, Miao Y, Zheng J. A YOLO-based intelligent detection algorithm for risk assessment of construction sites. Journal of Intelligent Construction, 2024, https://doi.org/10.26599/JIC.2024.9180037

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Received: 07 February 2024
Revised: 12 May 2024
Accepted: 19 May 2024
Published: 13 September 2024
© The Author(s) 2024. Published by Tsinghua University Press.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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