SFE-DETR:一种基于变压器的增强面部探测器,用于在开放复杂场景中的小目标面部
Chenhao Yang1, Yueming Jiang1, Chunyan Song1
1School of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究介绍了SFE-DETR,它是用于复杂场景的高效小型面部探测器. 它增强了特征保护和融合,实现了高精度,同时减少了改进计算机视觉应用的参数.
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.
背景情况:
- 在复杂场景中检测小人脸会带来诸如遮和尺度变化等挑战.
- 现有的方法在密集,退化的环境中难以获得准确性和效率.
研究的目的:
- 为开放,复杂的场景开发一个有效和高效的小型面部探测器.
- 为了同时提高检测准确度和计算性能.
主要方法:
- 采用了反转的残余转移卷积和扩展的重定量化骨干.
- 包含一个多头多尺度自我注意力机制,用于功能融合.
- 引入了重新设计的SFE-FPN,具有高分辨率层和一个新的功能融合模块.
主要成果:
- 与RT-DETR-R18.18相比,SFE-DETR的参数减少了28.1%,而RT-DETR的参数则减少了18%.
- 在SCUT-HEAD数据集上实现了94.7%的mAP50和42.1%的AP-s.
- 在WIDER FACE (硬) 子集上达到86.3%的mAP50.
结论:
- 在具有挑战性的条件下,SFE-DETR在小面部检测方面表现出卓越的检测性能.
- 该模型实现了其规模的最佳结果,平衡了准确性和效率.
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