基于可见光和红外光的混凝土高速公路裂纹检测合图像的酸盐光谱
Jian Xing1, Ying Liu1, Guangzhu Zhang2
1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
这项研究引入了一种使用可见和红外图像的新青路面裂检测方法,提高了照明条件差的情况下的准确性. 交叉模式融合技术增强了早期结构恶化检测.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 青路面的恶化通常首先是由裂表明的.
- 目前的裂检测依赖于可见光和卷积神经网络,将使用限制在白天和良好的条件下.
- 红外频谱数据为可见光用于裂检测提供了一个补充方法.
研究的目的:
- 提出一种新的跨模式特征对齐技术,用于使用可见和红外图像检测青路面裂.
- 在不同的照明条件下增强裂检测能力.
- 提高裂纹检测系统的准确性和稳定性.
主要方法:
- 开发了基于YOLOV5的交叉模式特征对齐技术,集成可见和红外图像.
- 引入了适应性照明感知重量生成模块,用于训练聚变网络.
- 提出了FA-BIFPN特征金字塔模块,以解决多尺度特征地图对齐问题.
- 为了高效的培训,采用了并行双脊柱网络结构.
主要成果:
- 融合图像方法在多个数据集 (FLIR,LLVIP,VEDAI) 中显示出比仅可见光图像更稳定的性能.
- 拟议的探测器性能优于现有的YOLOV5单模探测器和CFT交叉模融合模块.
- 在公共数据集上在微弱照明下检测5像素裂时,获得了98.3%的准确性.
结论:
- 可见和红外图像的交叉模式融合显著提高了青路面裂检测可靠性.
- 拟议的自适应照明感知模块和FA-BIFPN有效地解决了特征对齐和变光方面的挑战.
- 这种方法为早期的结构恶化检测提供了强大的解决方案,即使在低光环境中也是如此.
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