TriNeXt:一个高效的三路融合模块,用于在物体检测中实现多级特征增强.
Xu Zhang1, Xufeng Yu2, Jiahao Feng2
1Affiliation College of Software Engineering, Xiamen University of Technology, Xiamen, China.
Science progress
|November 18, 2025
概括
一个新的模块,TriNeXt,通过整合本地,嵌套和全球功能来增强对象检测. 它显著提高了在现实世界中小物体的检测精度和回忆.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 对象检测器可以检测到物体.
背景情况:
- 对象检测框架需要强大的多尺度特征表示.
- 增强空间和语义特征丰富对于提高检测准确度至关重要.
- 现有的方法可能难以有效地检测小物体.
研究的目的:
- 提出一个新的功能融合模块,TriNeXt,以增强对象检测中的多尺度表示.
- 整合本地,嵌套和全球上下文意识的功能丰富路径.
- 评估TriNeXt在提高物体检测性能方面的有效性,特别是对于小物体.
主要方法:
- 一个新的功能融合模块TriNeXt被设计并集成到YOLOv5s和YOLOv8s对象检测框架中.
- TriNeXt结合了本地,嵌套和全球上下文意识的路径.
- 在Cityscapes和KITTI数据集上进行了实验,以评估性能.
主要成果:
- 在TriNeXt-Full上,平均精度 (mAP) @0.5从YOLOv5s上的61.7%提高到63.2%,并在YOLOv8s上从62.3%提高到63.2%.
- 回忆率从54.5%增加到55.9% (YOLOv5s),精度从77.2%增加到79.2% (YOLOv5s).
- 在KITTI数据集中,TriNeXt-Full将mAP@0.5从94.0%提高到95.0%,回忆率从87.3%提高到90.1%,精度从95.2%提高到95.7%.
结论:
- 通过有效丰富多尺度表示,TriNeXt显著提高了对象检测性能,特别是对于小型对象.
- 该模块在检测准确度和实时推断速度之间实现了有利的平衡.
- TriNeXt在各种城市驾驶数据集中展示了强大的概括能力,为真实世界的应用建立了它的多功能性.
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