基于内容引导特征融合和自我校准的交叉模式对象检测
Liyang Ning1, Xuxun Liu1,2, Luoyu Zhou1
1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究引入了一种用于跨模式对象检测的新型双脊柱模型,通过整合变压器和卷积操作来增强特征表示和准确性. 拟议的方法显著提高了在不同环境中的检测性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的变压器在局部注意力方面存在局限性,这阻碍了特征表示和跨模态对象检测中的准确性.
- 深层特征可以通过卷积层降解,导致关键物体细节的损失.
研究的目的:
- 开发一个先进的双脊柱交叉模式物体检测模型.
- 在现有模型中克服局部注意力和特征退化方面的局限性.
主要方法:
- 在骨干中引入了一个并行网络,用于同时进行多模式处理.
- 一个内容引导融合 (CGF) 模块结合了变压器和卷积,用于全球和本地特征提取.
- 一个自适应校准融合 (ACF) 模块合并了浅层和深层特征,以保留细粒度的细节.
主要成果:
- 该模型在LLVIP数据集上获得了96.4的mAP50和63.8的mAP95.
- 在M3FD数据集中,该模型达到83.7的mAP50和56.6.6的mAP95.
- 在检测准确度方面表现优于基线和最先进的方法.
结论:
- 拟议的双脊柱模型有效地增强了跨模式对象检测.
- 新型融合模块在复杂场景中提高了特征表示和检测精度.
- 在各种环境中展示了强大的性能,用于跨模态物体检测任务.
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