SSRT-DETR:域自适应性半监督探测器
Wenshuai Zhang1, Dong Zhou1, Wenjie Xie1
1The Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了SSRT-DETR,这是一个新的半监督,域适应性对象检测框架. 它通过改善对域转移的匹配和伪标签策略来提高具有挑战性的数据集的性能.
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:
- 域自适应对象检测对于现实应用至关重要,但在域移动时面临挑战.
- 现有的方法与匈牙利匹配灵敏度和固定的伪标签值扎,特别是与阶级不平衡和场景变化.
研究的目的:
- 开发一个强大的半监督,域适应物体检测框架 (SSRT-DETR),克服当前方法的局限性.
- 通过提高匹配稳定性和自适应伪标签,在域移动下增强对象检测性能.
主要方法:
- 利用一个平均的老师-学生架构与风格转移的图像,用于联合领域建模.
- 引入域识别匹配 (DAM) 通过增加匈牙利匹配来稳定早期跨域培训.
- 开发了类/场景适应性伪标签 (CAP),以根据类和场景特征动态调整值.
主要成果:
- 在诸如Cityscapes→Foggy Cityscapes (mAP@0.5从51.0到54.3) 等基准上,SSRT-DETR显著提高了检测性能.
- 在KITTI→Cityscapes和Sim10K→Cityscapes上实现了用于汽车检测的最先进的结果 (67.3 AP和64.9 AP).
- 在罕见类别和恶劣天气中取得了持续的收益,验证了DAM和CAP的有效性,同时保持了实时效率.
结论:
- 拟议的SSRT-DETR框架有效地解决了域自适应对象检测的挑战.
- DAM和CAP模块是关键的创新,可以在多样化和具有挑战性的场景中实现强大的性能.
- SSRT-DETR为实时,域自适应对象检测提供了一个有前途的解决方案.
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