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ORIGINAL RESEARCH article

Front. Built Environ.

Sec. Construction Management

Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1563483

SD-YOLOv5: A Rapid Detection Method for Personal Protective Equipment on Construction Sites

Provisionally accepted
chunya li chunya li 1,2Jianhua Wang Jianhua Wang 2,3*Bingfeng Luo Bingfeng Luo 4TUBING YIN TUBING YIN 3Baohua Liu Baohua Liu 2,3Jianfei Lu Jianfei Lu 3
  • 1 School of Management, Wuhan University of Technology, Wuhan, Hubei Province, China
  • 2 Shenzhen Yantian Port Real Estate Co. Ltd, shenzhen, China
  • 3 Central South University, Changsha, China
  • 4 Shenzhen Port Group Co. Ltd, Shenzhen, China

The final, formatted version of the article will be published soon.

    With the rapid growth of urbanization, construction sites are increasingly confronted with severe safety hazards. Personal protective equipment (PPE), such as helmets and safety vests, plays a critical role in mitigating these risks; however, ensuring proper usage remains challenging. This paper presents SD(Small object detection and DilateFormer attention mechanism)-YOLOv5s, an improved PPE detection algorithm based on YOLOv5s, designed to enhance the detection accuracy of small objects, such as helmets, in complex construction environments. The proposed model incorporates a dedicated feature layer for small target detection and integrates the DilateFormer attention mechanism to balance detection performance and computational efficiency. Experimental results on the CHV dataset demonstrate that SD-YOLOv5s achieves an average precision (AP) of 93.7%, representing an improvement of 2.8 percentage points over the baseline YOLOv5s (AP = 90.9%), while reducing the model's parameter count by up to 14.6%. These quantitative improvements indicate that SD-YOLOv5s is a promising solution for real-time PPE monitoring on construction sites, although further validation on larger and more diverse datasets is warranted.

    Keywords: Personal protective equipment (PPE) detection, YOLOv5, Small object detection, DilateFormer attention mechanism, Construction site safety

    Received: 20 Jan 2025; Accepted: 26 Mar 2025.

    Copyright: © 2025 li, Wang, Luo, YIN, Liu and Lu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jianhua Wang, Central South University, Changsha, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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