注意力双流互动感知网络,用于高效的红外小型空中目标检测
1College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, PR, China.
概括
本研究介绍了注意力双流交互感知网络 (ADIPNet),用于改进红外小目标检测. ADIPNet 增强了特征提取,从而在复杂的环境中更准确地识别空中目标.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 红外成像技术 红外成像技术
背景情况:
- 远程红外成像与小型空中目标相斗争,特点是细节丢失和低检测效率.
- 现有的方法在深度提取小目标特征方面面临挑战,特别是在遮蔽和环境干扰下.
研究的目的:
- 提出一个先进的深度学习网络,注意力双流交互感知网络 (ADIPNet),用于增强红外小目标检测.
- 解决当前红外小目标识别系统的不足特征提取和低效率的局限性.
主要方法:
- 拟议的ADIPNet基于双流U-Net架构,包括多补丁系列并行注意 (MSPA),边缘与遗憾 (EAR) 定,上下文场景感知 (CSP) 和双流交互融合 (DSIF) 模块.
- MSPA通过多尺度补丁权重和嵌套的自我注意力来挖掘全球目标信息.
- EAR完善了边缘检测,CSP通过上下文交换增强了功能感知,DSIF通过交叉注意力来改进复杂场景理解的功能.
主要成果:
- ADIPNet显著减轻了红外小目标的不足特征提取.
- 该网络在两个大型红外数据集上实现了平均交叉在欧盟 (mIoU) 的分数为80.52%和72.54%,超过了最先进的方法.
- 经过精确检测,能够在低运营成本下准确地探测小型空中目标.
结论:
- ADIPNet为红外小型目标检测提供了强大的解决方案,提高了准确性和效率.
- 该网络深度提取特征和理解复杂场景的能力显示出在各种红外监控系统中应用的巨大潜力.
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