.png){kind=icon}
返回
Innovative computer vision-based full-scale timber element cracks detection, stitching, and quantification
.png){kind=icon}
PRE
AI
DOI：10.1177/14759217241258682.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Timber is susceptible to environmental humidity variations, inevitably resulting in cracks parallel to the wood grain during the service life. Cracks significantly degrade the effective cross-sectional area and seriously affect structural safety and durability. Therefore, it is significant to identify the timber elements' cracking conditions for providing reliable maintenance. Existing timber structure crack inspection mainly relies on manual work. However, with the rapid development of high-rise and large-span glued timber structure, manual-based crack inspection is not applicable to such structures for increasing workload and uncontactable high-altitude timber elements. In order to make up for the deficiencies of the existing crack detection algorithms, this paper proposed an innovative computer vision-based method inspecting full-scale timber column cracks. In step one, the crack images were stitched to exhibit the full-scale cracking condition. In step two, the YOLOv5 model was trained utilizing 425 images collected from cracked timber structures and performed K-fold crossover validation algorithm. In step three, cracking regions are quantified at the physical level. Field tests showed that the proposed method has a crack identification precision better than 0.2 mm and error below 5% compared with manual measurement, which can provide high-precision, time-saving, and noncontact in-situ crack inspection for timber structures.
Keyword:
Computer vision
timber element
crack detection
image stitching
crack quantification
期刊
S
IF:
5.7
论文数:
2.3K
被引数:
1.1W
机构
引用论文
SGN-YOLO: Detecting Wood Defects with Improved YOLOv5 Based on Semi-Global NetworkSgn-yolo: 基于半全局网络的改进YOLOv5木材缺陷检测.png)
SENSORS
OAAI
SENSORSIF3.5
OAAI分享收藏A two-step computer vision-based framework for bolt loosening detection and its implementation on a smartphone application基于两步计算机视觉的螺栓松动检测框架及其在智能手机上的实现
PREAI
分享收藏Automated Asphalt Highway Pavement Crack Detection Based on Deformable Single Shot Multi-Box Detector Under a Complex EnvironmentIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏A deep learning approach for fast detection and classification of concrete damage一种用于混凝土损伤快速检测和分类的深度学习方法
PREAI
分享收藏
查看更多内容