MEAC:一个多尺度的边缘感知卷积模块,用于强大的红外小目标检测
Jinlong Hu1, Tian Zhang2, Ming Zhao3
1Institute of Seismology, China Earthquake Administration, Wuhan 430071, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
一个新的多尺度边缘感知卷积 (MEAC) 模块通过增强特征表示来显著改善红外小目标检测. 这种方法在复杂的背景中表现出色,在精度和稳定性方面优于现有的卷积模块.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 红外小目标检测对于军事,环境和安全应用至关重要.
- 传统的卷积神经网络 (CNN) 面临着由于特征提取有限的小,低对比度目标的挑战.
- 复杂的背景和低的信号噪声比率进一步使检测复杂化.
研究的目的:
- 引入一个新的多尺度边缘感知卷积 (MEAC) 模块,用于增强红外小目标检测.
- 为了改善特征表示,而不增加计算成本或参数数量.
- 解决现有的CNN在检测弱小红外目标方面的局限性.
主要方法:
- 提出了多尺度边缘意识转换 (MEAC) 模块.
- MEAC融合了局部特征,通过扩展卷积的多尺度上下文,以及来自差异高斯过器的边缘线索.
- 集成的通道和空间注意力机制,用于适应性特征强调.
主要成果:
- 用MEAC增强的网络在三个公共数据集 (SIRSTD-UAVB,IRSTDv1,IRSTD-1K) 上显著超过了基线模型.
- 与11个主流卷积模块相比,MEAC实现了更高的检测精度和稳定性.
- 在YOLOv10,YOLOv11和YOLOv12架构中观察到一致的性能改进.
结论:
- 该MEAC模块有效地提高了小,弱红外目标的检测.
- 在复杂的场景中,MEAC表现出强大的概括能力和实际应用潜力.
- 拟议的模块在抑制背景噪声和提高检测精度方面具有显著的优势.
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