.png){kind=icon}
返回
Welding defects classification by weakly supervised semantic segmentation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ndteint.2023.102899.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Radiographic non-destructive evaluation (NDE) is an essential technique for understanding defects in welds. These radiographs require certified workers to interpret them to identify the presence of defects. Recent deep learning techniques, primarily semantic segmentation, could help welding defect detection and classification. Using image segmentation technology to obtain performance evaluations of the presence, location, and size of defects can improve the stability of defect evaluations while saving a great deal of time. However, supervised instance segmentation requires many manually implemented pixel-level annotations, dramatically increasing the difficulty and cost of achieving non-destructive evaluations. In our work, the weakly supervised semantic segmentation based on the Cut-Cascade RCNN model is used to classify defects. The cascade RCNN obtains the region of interest (ROI) and classification information. In the ROI, adaptive threshold segmentation of the defects is implemented, and the image is filtered to obtain the mask information. The accuracy of using the Cut-Cascade RCNN model in our x-ray dataset size can reach 90.15%.
Keyword:
Deep learning
Non -destructive evaluation
Welding defect
期刊
N
IF:
4.5
论文数:
3.1K
被引数:
9.0K
机构
引用论文
.png){kind=icon}
DFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution ModelDffir-net: 灰度分布模型约束下的红外弱小目标检测网络
PREAI
分享收藏Anomalous Spin-Resolved Point-Contact Transmission of Holes due to Cubic Rashba Spin-Orbit Coupling
OAAI
OAAI分享收藏
查看更多内容