一个红外无人机视角车辆实例细分模型,具有强大的适应性和完整性
Peng Huang1, Yan Yin1, Kaifeng Hu1
1National Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430000, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了MonoSeg,这是一个先进的实例细分框架,用于使用无人飞行器 (UAV) 的红外成像检测车辆. MonoSeg提高了检测准确性和效率,克服了诸如视角变化和低对比度等挑战.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 基于无人驾驶飞行器 (UAV) 的红外车辆检测面临着视角变化,平台不稳定,低对比度和热交叉效应等挑战.
- 边缘计算平台需要高效的算法来平衡实时处理与检测性能.
研究的目的:
- 介绍一下MonoSeg,这是一种新的实例细分框架,专门设计用于UAV视角红外探测车辆.
- 在具有挑战性的红外成像条件下提高检测精度和计算效率.
主要方法:
- 开发了Ghost Feature Bottle Cross (GFBC) 模块,以提高骨干特征提取效率并减少计算开销.
- 引入了Scale Feature Recombination (SFR) 模块,用于在Neck阶段适应式的多尺度特征融合.
- 实现了一个全面损失函数,用于精确的实例边界划分.
主要成果:
- 在基准数据集上,MonoSeg 实现了最先进的性能,高的 Box mAP 和 Mask mAP 评分证明了这一点.
- 与现有方法相比,该框架的计算要求明显较低.
- 成功应对动态视角和红外成像局限性所带来的挑战.
结论:
- 在无人机基础的红外车辆检测中,MonoSeg提供了显著的进步,提供了高精度和效率.
- 拟议的模块和损失函数有效地增强了特征提取,融合和边界划分.
- MonoSeg 提供了一种可行的解决方案,用于使用边缘计算平台在复杂环境中实时检测车辆.
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