基于变压器的路面裂检测的多尺度特征融合
Yalong Yang1,2,3, Zhen Niu1,2,3, Liangliang Su1,2,3
1Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University, Hefei 230022, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
这项研究介绍了一种新的基于变压器的网络,用于自动化地板裂检测. 该方法增强了特征提取和多尺度融合,在各种照明条件下提高了准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 自动路面裂检测是具有挑战性的,因为细的裂识别和不同的照明.
- 现有的方法与复杂的背景和照明不一致性作斗争.
研究的目的:
- 提出基于变压器架构的新型多级特征融合网络,用于改进自动化路面裂检测.
- 增强特征提取和上下文获取,以实现更准确的裂细分.
主要方法:
- 在编码阶段使用了变压器架构,用于全球建模和远距离依赖.
- 采用扩展卷积来扩大受体场,同时保持特征地图分辨率.
- 在解码阶段实施多级特征融合,以恢复详细的破解信息.
主要成果:
- 在Crack500数据集上获得70.84%的F1得分,比最先进的数据集提高了1.42%.
- 在DeepCrack数据集上获得了84.50%的F1得分,比最先进的数据集提高了2.07%.
- 在高低亮度条件下表现出卓越的检测准确性和概括性.
结论:
- 拟议的基于变压器的多尺度特征融合网络显著提高了自动化路面裂检测的准确性.
- 该方法在具有挑战性的照明条件下表现出强大的性能,优于现有的最先进的方法.
- 这种方法为路面裂分析提供了更好的概括能力.
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