SAM2-DFBCNet:一个伪装物体检测网络,基于SAM2的Heira架构
Cao Yuan1, Libang Liu1, Yaqin Li1
1School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430040, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了SAM2-DFBCNet,这是一个用于伪装物体检测 (COD) 的新型网络. 它显著提高了对象混合到复杂的背景的细分精度,优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像细分 图像细分
背景情况:
- 伪装物体检测 (COD) 面临着低对比度,复杂纹理和模糊边界等挑战.
- 现有的深度学习方法在这些困难的场景中难以进行强大的细分.
研究的目的:
- 提出SAM2-DFBCNet,这是一个新的网络,旨在克服当前伪装物体检测技术的局限性.
- 为了提高在具有挑战性的视觉环境中对伪装物体进行细分的准确性和稳定性.
主要方法:
- 在SAM2 Hiera架构基础上开发了SAM2-DFBCNet.
- 整合了三个关键模块:伪装意识上下文增强模块 (CACEM),跨度特征交互桥 (CSFIB) 和动态边界改进模块 (DBRM).
- 利用注意力机制和双向卷积GRU用于特征融合和边界精细化.
主要成果:
- 在CAMO,COD10K和NC4K数据集上,SAM2-DFBCNet的性能超过了二十种最先进的方法.
- 在Sα方面取得了7.4%,Fβ方面5.78%,Eφ方面4.78%的最大改善.
- 降低了37.8%的平均绝对误差 (M).
结论:
- 拟议的SAM2-DFBCNet在伪装物体检测方面表现出卓越的性能和稳定性.
- 该网络有效地解决了低对比度,复杂纹理和伪装场景中模糊边界所带来的挑战.
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