地质U-SEA-YOLO:用于检测建筑工地不安全行为的先进模型
Xuejun Jia1,2, Xiaoxiong Zhou1, Zhihan Shi1
1College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了GeoIoU-SEA-YOLO,这是一个用于施工现场安全的先进物体检测模型. 它有效地识别了不安全的行为,如丢失的安全装备,改善了事故预防.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 建筑安全工程工程 建筑安全工程
背景情况:
- 由于不安全的行为,建筑工地存在重大安全风险.
- 传统的安全监控方法缺乏实时检测和全面覆盖.
- 事故往往是由于不遵守安全协议造成的,例如缺少个人防护设备.
研究的目的:
- 开发一种增强的物体检测模型,用于实时识别建筑工地上的不安全行为.
- 提高复杂建筑环境中的安全监控系统的准确性和效率.
- 解决现有方法在检测小物体,遮蔽和多物体相互作用方面的局限性.
主要方法:
- 介绍了GeoIoU-SEA-YOLO,一个物体检测模型.
- 整合几何交叉在联盟 (GeoIoU) 损失的完善的边界框回归.
- 整合结构增强注意力 (SEA) 机制,以改进特征地图权重.
- 对YOLOv3,YOLOv5s,YOLOv8s和SSD等既有模型进行比较实验.
主要成果:
- GeoIoU-SEA-YOLO实现了卓越的性能,平均平均精度 (mAP@0.5) 为0.930.
- 该模型表现出高精度和回忆力,在检测小物体和隐藏实例方面表现出色.
- 能够有效地识别特定的不安全行为,包括缺失安全头盔,安全背心和吸烟情况.
结论:
- GeoIoU-SEA-YOLO为建筑领域的智能安全管理提供了可靠和准确的解决方案.
- 结合的 GeoIoU 和 SEA 机制大大提高了对象检测性能.
- 该模型为实时监控和预防不安全行为造成的事故提供了一个强大的工具.
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