使用BSM-YOLO在没有空中服务悬挂的施工期间进行FOD检测研究
1School of Management Science and Real Estate, Chongqing University, Chongqing, 400045, China. 1391411123@qq.com.
Scientific reports
|November 26, 2025
概括
在机场建设过程中检测外来物体碎片 (FOD) 是至关重要的. 新的BSM-YOLO模型在复杂的环境中增强了多层次检测,提高了安全性,而不会破坏航空服务.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 机场扩建需要在施工过程中检测外来物体碎片 (FOD).
- 传统的FOD检测模型难以应对复杂的建筑环境和多个规模的目标.
- 目前的方法往往需要暂停航空服务,影响运营.
研究的目的:
- 引入BSM-YOLO模型,在复杂的机场建设场景中改进FOD检测.
- 提高多尺度目标识别和适应图像变化的能力.
- 在施工期间提供安全,连续的机场运营解决方案.
主要方法:
- 增强的双向特征金字塔网络 (BIFPN) 具有独立的金字塔分支和快速规范化,以优化特征融合.
- 嵌入了Sic2f结构,在骨干中关注相似性,以提高对复杂图像变化的适应性.
- 引入了部水平的Mc2f结构,使用多尺度卷积和频道注意力来捕获空间频率信息.
主要成果:
- 与YOLOv8n模型相比,BSM-YOLO模型的平均精度 (mAP) 提高了5.3%.
- 在复杂的建筑环境中,在多尺度FOD检测中表现出卓越的性能.
- 在需要不间断航空服务的场景中验证了该模型的有效性.
结论:
- BSM-YOLO模型显著提高了FOD检测的准确性和稳定性.
- 提供了一个可行的解决方案,在机场建设期间提高安全性,而不会暂停服务.
- 在航空基础设施项目中促进人工智能驱动的安全解决方案的发展.
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