LHAT-YOLO:研究建筑工地头盔的智能监控算法
Jun Wang1, Dongsheng Zhao1, Haoran Jiang2
1Sichuan Institute of Industrial Technology, Deyang, Sichuan, China.
PloS one
|January 28, 2026
概括
本研究介绍了LHAT-YOLO,这是一种用于在建筑工地实时检测头盔的深度学习模型. 轻量级模型通过有效监控头盔的合规性,减少事故和改善工人保护,提高了安全性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 建筑工地面临着重大安全风险,特别是头部受伤.
- 手动监测头盔的合规性是低效的,缺乏实时功能.
- 现有的智能监控模型很难满足实际的实时需求.
研究的目的:
- 提出在土木工程施工现场使用头盔的智能实时监控方法.
- 开发一个轻量级的深度学习模型,保持准确性以进行高效的检测.
- 解决传统和现有的智能监控系统的局限性.
主要方法:
- 利用深度学习理论,特别是通过GSConv.改进YOLOv11模型.
- 引入了一个轻量级的检测头,快速卷积检测 (FCD),以创建LHAT-YOLO模型.
- 在一个包含24,526张图像的综合数据集上训练和测试模型.
主要成果:
- 与YOLOv11.11相比,LHAT-YOLO模型实现了GFLOP的11%降低和参数的9.5%降低.
- 证明了高性能,精度为93.95%,回忆率为88.99%,mAP50为94.92%,mAP50-95为65.28%.
- 该模型提供高效和智能检测,同时保持轻量级.
结论:
- 在土木工程项目中,LHAT-YOLO模型为实时头盔检测提供了有效的解决方案.
- 轻量级的设计确保了高精度和效率,这对于实际的现场应用至关重要.
- 这一进步有助于通过智能监控提高建筑工地安全和工人保护.
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