强大的红外可见融合成像与脱语义细分网络
Xuhui Zhang1,2, Yunpeng Yin1,2, Zhuowei Wang1,2
1Guangdong Provincial Key Laboratory of Cyber-Physical System, Guangdong University of Technology, Guangzhou 510006, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一个用于融合红外和可见图像的新网络,提高了低光条件下的性能,并提高了海上监视的目标检测精度.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 人工智能的人工智能
背景情况:
- 红外和可见图像融合为监视和军事应用提供了补充数据.
- 现有的核聚变方法往往缺乏客观评估,无法有效应对低光条件.
- 高级视觉任务的执行通常独立于主观的融合质量指标.
研究的目的:
- 开发一种新的脱和语义细分驱动的网络,用于红外和可见图像融合.
- 在具有挑战性的低光场景中提高融合质量和性能.
- 提高海上环境中的目标检测和整体准确性.
主要方法:
- 一个跨模式的变压器融合模块用于层次特征学习.
- 一个语义驱动的融合模块,以强调突出的目标特征.
- 一个加权的核聚变策略和对自然核聚变图像的精细损失函数.
主要成果:
- 拟议的方法在公共和新的海事红外和可见 (MIV) 数据集上实现了优越的融合质量指标.
- 实现了超过96%的目标检测准确度和高mAP@[50:95]值.
- 在实际的海上环境感知任务中表现出强度和概括性.
结论:
- 以语义细分为驱动的方法有效地将融合与下游任务联系起来.
- 该方法在客观指标和实际应用方面显著优于现有方法.
- 新的MIV数据集有助于对海上监视进行评估.
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