MSIA-Net:一个轻量级的红外目标检测网络,具有高效的信息融合
Jimin Yu1, Shun Li1, Shangbo Zhou2
1College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
一个新的轻量级网络,MSIA-Net,通过减少模型大小和提高精度来解决红外目标检测方面的挑战. 它使用新的模块来进行特征提取,下方采样和融合,提高目标聚焦和检测性能.
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:
- 红外目标检测面临着大型模型和众多参数的挑战.
- 现有的方法经常在下方采样过程中损失信息,在特征融合过程中产生噪声.
研究的目的:
- 提出一个轻量级和高效的红外目标检测网络,MSIA-Net.
- 为了减少模型的复杂性,同时提高检测准确性和目标焦点.
主要方法:
- 引入了MSIA (多尺度不对称) 模块,用于使用不对称卷积进行高效的特征提取.
- 开发了DPP (Down-sampling with Pooling) 模块,以尽量减少在Down-sampling过程中的信息丢失.
- 拟议的LIR-FPN (轻量级信息回收特征金字塔网络) 以减少噪音有效的特征融合.
- 在LIR-FPN中集成了Coordinate Attention (CA),以改善目标定位和特征表示.
主要成果:
- 与现有方法相比,MSIA-Net显示了模型参数的显著减少.
- 拟议的模块有效地减少了处理过程中的信息丢失和噪音.
- 集成CA增强了网络专注于目标特征的能力.
- 在FLIR机载红外图像数据集上的比较实验验证实了卓越的检测性能.
结论:
- MSIA-Net为红外目标检测提供了强大而高效的解决方案.
- 新型架构组件有助于提高性能和降低计算成本.
- 该网络显示了现实世界的红外成像应用的巨大潜力.
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