TCCDNet:一个多模式的行人检测网络,集成跨模式互补性与深度特征融合
Shipeng Han1, Chaowen Chai1,2, Min Hu1
1School of Biomedical Engineering, Air Force Medical University, Xi'an 710032, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究介绍了TCCDNet,一个新的多式联网行人检测网络. TCCDNet有效地融合了红外和可见数据,大大提高了复杂环境中的检测准确性.
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:
- 多模式行人检测对于复杂的场景至关重要.
- 利用红外和可见光的互补性提高了性能.
- 跨模式融合和跨模式互补性探索仍然存在挑战.
研究的目的:
- 提出TCCDNet,一个用于多式联网行人检测的新型网络.
- 通过交叉模式的互补性来增强特征提取和融合.
- 为了提高对象定位准确性和特征可区分性.
主要方法:
- 开发了高效的多尺度关注C2f (EMAC) 骨干,用于特征加权和融合.
- 引入了带有频道和空间注意力的交叉模式互补性 (CMC) 模块.
- 整合了一个深度语义融合模块 (DSFM),使用交叉注意力进行深度语义融合.
主要成果:
- 在KAIST数据集上,TCCDNet实现了7.87%的MR^2,表现比YOLOv8高3.83%.
- 在FLIR ADAS上达到83.8%的mAP50和在LLVIP上达到97.3%的mAP50,分别超过了3.6%和1.9%的基线.
结论:
- TCCDNet在多式联网行人检测方面取得了重大进展.
- 拟议的融合机制有效地利用了模式间的互补性.
- 该网络在多个基准数据集中显示出卓越的性能.
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