.png){kind=icon}
返回
Patch-based weakly supervised semantic segmentation network for crack detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.conbuildmat.2020.120291.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Obtaining spatial and topological information for cracks in construction materials is important for the evaluation of service performance in infrastructure engineering. Manually extracting crack information from an image is a tedious process. Recently, popular deep learning-based semantic segmentation technologies have been employed to alleviate this problem. However, existing semantic segmentation methods for crack detection are fully supervised, i.e., these methods require manual annotation of data to obtain pixel-level labels for training, which is time-consuming. To solve this problem, this paper proposes a patch-based weakly supervised semantic segmentation network for crack detection. The proposed method uses image-level annotation as the supervision condition and fully considers the local similarity of the crack topology in the image. The use of patches cropped from the image as the input in this method can reduce the image complexity significantly without losing the spatial location information of the crack. A discriminative localization technique is used to extract rough location information of the crack from a trained classification network, which is then refined by a conditional random field to obtain a synthetic label. These synthetic labels can replace the manually annotated pixel-level labels for the training of the segmentation network. Thereafter, a neighborhood fusion strategy is used to merge the patches into the final output. Two datasets are employed to train and evaluate the proposed method. The results indicate that this method can achieve a performance comparable to that of fully supervised methods (an MIoU of 0.821 and F-score of 0.8 were obtained for the fully supervised method compared with an MIoU of 0.782 and F-score of 0.741 with the proposed method), while reducing the annotation workload by approximately 80%. (C) 2020 Published by Elsevier Ltd.
Keyword:
Crack detection
Deep learning
Weakly supervised learning
Semantic segmentation
Computer vision
Convolutional neural network
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:
8
论文数:
4.5W
被引数:
27.9W
机构
暂无机构信息
引用论文
Patient quality of recovery on the day of surgery after propofol total intravenous anesthesia for vitrectomy.png)
Medicine
OAAI
MedicineIF0
OAAI分享收藏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DeepCrack: A deep hierarchical feature learning architecture for crack segmentationNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容