DT-YOLO:基于YOLOv5的机场场景中的飞机和机组人员关键部件的改进物体检测算法
Zhige He1, Yuanqing He1, Yang Lv2
1School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究介绍了DT-YOLO,这是一种用于机场前台的增强物体检测模型,可显著提高关键航空安全应用的准确性和速度. 新型号提高了平均精度 (mAP),以更好地检测飞机部件和人员.
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:
- 准确检测飞机部件和机场机台上的人员对于飞行安全和运营效率至关重要.
- 现有的机场环境对象检测模型在准确性和可用性方面存在局限性.
- 民用航空日益增长的需求需要先进的检测解决方案.
研究的目的:
- 提出一个改进的物体检测算法,DT-YOLO,基于YOLOv5,以提高机场平台场景中的性能.
- 为了解决当前在这一领域的检测模型的稀缺性和不充分的准确性.
- 通过先进的视觉检测,提高机场运营的安全性和效率.
主要方法:
- 从真实世界机场监控视频开发了一个新的数据集,AAD-数据集.
- 引入了一个D-CTR模块,将变压器全球特征提取与CNN集成在一起,以增强特征表示.
- 整合了一层掉落层以减少噪音和防止过,并利用可变形的卷积来进行多尺度物体检测.
- 修改了GIoU Loss函数以提高稳定性和减轻梯度问题.
主要成果:
- 在AAD数据集上,DT-YOLO在平均平均精度 (mAP) 中显著提高了2.6%.
- 与基线模型相比,该模型显示了增强的检测速度,AP50和AP75.
- 实验结果验证了拟议的D-CTR模块和损失函数修改的有效性.
结论:
- DT-YOLO为复杂的机场平台环境中的实时物体检测提供了强大的解决方案.
- 该算法有助于确保更安全的飞机操作和更有效的机场管理.
- 该研究强调了将变压器和CNN功能集成为专门的物体检测任务的潜力.
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