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Detection of helmet use among construction workers via helmet-head region matching and state tracking
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-24 , DOI: 10.1016/j.autcon.2025.105987
Yi Zhang, Shize Huang, Jinzhe Qin, Xingying Li, Zhaoxin Zhang, Qianhui Fan, Qunyao Tan

Accidents at construction sites are prevalent, posing a significant safety threat to workers. Helmets play a crucial role in protecting workers' heads during accidents, and helmet wearing monitoring is essential for ensuring workers' safety. However, it becomes challenging to detect whether workers are wearing helmets when their heads are obstructed or invisible. To enable continuous and accurate monitoring of workers' helmet-wearing states, this paper proposes a method based on the YOLOv9 object detection algorithm, the YoloPose human pose estimation model, and the StrongSORT tracking algorithm for helmet-wearing detection. The keypoints detected by YoloPose are used to extract the head region and are subsequently matched with a helmet bounding box detected by YOLOv9. Based on the tracking results of workers, matching information from preceding frames is integrated to update workers' helmet-wearing states. The proposed algorithm achieves 98.89 % accuracy on the self-built dataset, significantly enhancing the consistency of helmet-wearing state detection.

中文翻译:


通过头盔头区域匹配和状态跟踪检测建筑工人的头盔使用情况



建筑工地的事故普遍存在,对工人构成重大安全威胁。头盔在事故中保护工人的头部起着至关重要的作用,头盔佩戴监测对于确保工人的安全至关重要。然而,当工人的头部被遮挡或看不见时,检测他们是否戴着头盔变得具有挑战性。为了实现对工人头盔佩戴状态的持续准确监测,本文提出了一种基于 YOLOv9 目标检测算法、YoloPose 人体姿态估计模型和 StrongSORT 跟踪算法的头盔佩戴检测方法。YoloPose 检测到的关键点用于提取头部区域,随后与 YOLOv9 检测到的头盔边界框匹配。根据 worker 的跟踪结果,整合前一帧的匹配信息,以更新worker 的头盔佩戴状态。所提算法在自建数据集上实现了 98.89% 的准确率,显著提高了头盔佩戴状态检测的一致性。
更新日期:2025-01-24
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