基于红外和可见图像融合和改进的YOLOv8算法的目标识别
Wei Guo1, Yongtao Li1, Hanyan Li2
1School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
这项研究引入了一种适应性照明感知融合机制,以改善红外和可见图像的融合,提高目标识别精度. 这种新的方法显著提高了对象检测任务的性能指标.
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对于增强情境意识至关重要.
- 现有的聚变方法往往容易受到照明条件的变化的影响.
- 强大的特征提取对于准确的目标识别至关重要.
研究的目的:
- 提出一种适应性照明感知融合机制,以减轻照明因素的影响.
- 将这种机制集成到红外和可见图像融合网络中.
- 为了提高在融合图像上训练的目标识别网络的性能.
主要方法:
- 空间注意力机制用于从红外和可见图像中提取特征.
- 深度卷积神经网络用于高级特征信息提取.
- 在图像重建中集成的自适应照明感知融合机制.
- 一个中位强化通道和空间注意模块 (MSCS) 融入YOLOv8骨干中.
- 为训练目标识别网络而创建了一个新的数据集 (ivifdata).
主要成果:
- 改进的YOLOv8网络在召回 (2.3%),mAP50 (1.4%) 和mAP50-95 (8.2%) 中显示出显著的改进.
- 融合网络有效地减少了合并图像中照明变化的影响.
- 增强的YOLOv8网络显示了更好的识别率和完整性.
- 该系统实现了虚假阴性和虚假阳性的减少.
结论:
- 拟议的自适应照明感知融合机制有效地解决了图像融合中的照明挑战.
- 将这种机制集成到YOLOv8中可以显著提高目标识别性能.
- 开发的核聚变网络和数据集有助于在多模式图像分析和物体检测方面的进步.
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