通过深度学习的联合融合和检测在无人机载的多光谱传感分散式地雷中
Zhongze Qiu1, Hangfu Guo1, Jun Hu1
1School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
无人驾驶飞行器 (UAV) 多光谱成像与新的深度学习融合策略显著改善可散射地雷的检测,特别是对于封闭的目标. 这种探测驱动的融合 (DDF) 提高了回忆率,并验证了用于雷区绘图的多谱数据处理.
科学领域:
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 雷区探测器 雷区探测器
背景情况:
- 传统的地雷检测方法与大面积和封闭目标作斗争.
- 基于无人机 (UAV) 的多谱成像为快速,广泛地雷检测提供了潜力.
- 将深度学习与多谱数据相结合,对于提高检测准确性至关重要.
研究的目的:
- 为基于无人机的散射地雷检测开发一个强大的多光谱融合战略.
- 提高检测性能和召回率,特别是对于隐藏的地雷.
- 创建一个检测驱动的融合 (DDF) 架构,以提高融合图像质量和处理速度.
主要方法:
- 使用无人机载平台建立了可散射地雷的多光谱数据集,考虑了植被.
- 采用主动学习来改进数据集标签,以改善封闭地雷检测.
- 提出了一种基于检测的融合架构,使用YOLOv5进行检测和轻量化融合网络进行细节聚合.
主要成果:
- 拟议的检测驱动的核聚变 (DDF) 战略显著提高了地雷检测的召回率.
- 该方法在识别隐藏的地雷方面表现出特别高的有效性.
- 实验验证实了处理多光谱数据用于探雷的可行性和有效性.
结论:
- 基于无人机的多谱成像与深度学习相结合,为快速准确地探测地雷提供了可行的解决方案.
- 开发的DDF方法增强了检测能力,特别是在具有挑战性的隐蔽目标场景中.
- 这项研究证实了多光谱数据处理对于有效地进行雷区监测和清除的有用性.
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