基于高分辨率功能增强的语义细分网络的红外飞机检测算法
Gang Liu1, Jiangtao Xi2, Chao Ma1
1College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究介绍了一种新的红外飞机检测算法,使用高分辨率功能增强的语义细分网络. 该方法在干扰条件下显著提高了检测精度,超过了现有的算法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 红外飞机检测对于安全和监视至关重要.
- 现有的方法与干扰作斗争,导致精度降低.
- 高分辨率的特征表示是稳健检测的关键.
研究的目的:
- 开发一个先进的红外飞机检测算法.
- 为了增强目标特征表示和上下文信息捕获.
- 在具有挑战性的干扰条件下提高检测性能.
主要方法:
- 提出了一个高分辨率功能增强的语义细分网络.
- 一个位置注意力特征融合网络增强了目标特征.
- 一个混合的atrous空间金字塔聚合模块捕获了上下文信息.
- 一个子损失函数优化了前景信息学习.
主要成果:
- 该算法实现了92.74%的平均交叉与结合 (mIoU).
- 平均像素精度 (mPA) 达到96.34%,平均回忆率 (MR) 为96.19%.
- 在自建的红外数据集上表现优于DeepLabv3+,Segformer,HRNetv2和DDRNet.
结论:
- 拟议的算法有效地检测干扰的红外飞机.
- 注意力机制和扩张卷曲的组合增强了特征表示.
- 该方法为红外监控应用提供了显著的进步.
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