跨域对象检测与分层多级域自适应YOLO
Sihan Zhu1, Peipei Zhu1, Yuan Wu1
1National Key Laboratory of Complex Aviation System Simulation, Southwest China Institute of Electronic Technology, Chengdu 610036, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了HMDA-YOLO,这是一种使用一阶段YOLO框架的新型域自适应物体检测方法. 它通过通过层次的骨干和多尺度的头部适应来解决域转移来提高实时检测性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 域位移降低了对象检测性能.
- 现有的域自适应物体检测 (DAOD) 方法经常使用低效的两级探测器.
- 需要有效的DAOD方法,适合现实世界的应用.
研究的目的:
- 提出一种与一级YOLO框架集成的新型层次多级域适应 (HMDA) 方法.
- 为了在目标域上提高对象检测性能,尽管域变化.
- 为了实现实时检测效率.
主要方法:
- 开发了HMDA-YOLO,这是一个单阶段域自适应物体检测方法.
- 实现了层次的骨干适应,以在不同网络深度上对齐功能分布.
- 集成的多尺度头部适应,以利用功能地图信息来改进检测.
主要成果:
- 在各种跨域对象检测场景中,HMDA-YOLO展示了竞争性性能.
- 该方法有效地减少了域间的分布差异.
- 在不影响准确性的情况下实现实时检测效率.
结论:
- HMDA-YOLO为域自适应对象检测提供了一个有效的解决方案.
- 拟议的层次和多层次的调整改进了一般化和歧视能力.
- HMDA-YOLO为现实世界的物体检测挑战提供了实用和高效的方法.
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