.png){kind=icon}
返回
Focusing on Cracks with Instance Normalization Wavelet Layer
.png){kind=icon}
OA
AI
DOI：10.3390/s25010146.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatic crack detection is challenging, owing to the complex and thin topologies, diversity, and background noises of cracks. Inspired by the wavelet theory, we present an instance normalization wavelet (INW) layer and embed the layer into the deep model for segmentation. The proposed layer employs prior knowledge in the wavelets to capture the crack features and filter the high-frequency noises simultaneously, accelerating the convergence of model training. Furthermore, instance normalization in our layer is utilized to mitigate the feature differences, boosting the generalization capability. In addition, a fusion layer is added to merge the information across the different layers. The comparison experiments and ablation studies demonstrate that the INW layer steadily enhances recognition and convergence performance on the DeepCrack dataset and CRACK500 dataset.
Keyword:
crack detection
wavelet
convolution neural networks
feature fusion
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete深度卷积神经网络和边缘检测器在基于图像的混凝土裂缝检测中的比较
PREAI
分享收藏
Distant regulatory elements in a Sox10‐βGEO BAC transgene are required for expression of Sox10 in the enteric nervous system and other neural crest‐derived tissuesSox10-βgeo BAC转基因中的远距离调控元件是肠神经系统和其他神经源性组织中 Sox10 表达所必需的OAAI
OAAI分享收藏DeepCrack: A deep hierarchical feature learning architecture for crack segmentation.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Modified dichromate method for determining low concentrations of extractable organic carbon in soil
PREAI
分享收藏
查看更多内容