Paper Info
Title
Automatic Detection And Location Of Weld Beads With Deep Convolutional Neural Networks
Abstract
Welding quality detection is a critical link in modern manufacturing, and the weld bead location is a prerequisite for the high-precision assessment of welding quality. It is generally necessary for weld bead detection to be accomplished in the context of complex industrial environments. However, conventional detection and location methods based on specific detection conditions or prior knowledge lack accuracy and adaptability. To precisely detect and locate the weld beads in real industrial environments, a novel weld bead detection and location algorithm is proposed based on deep convolutional neural networks. Because there is no open data set of weld beads and the samples in real industrial applications are insufficient for effective model training of the deep convolutional neural network, a novel data augmentation method based on a deep semantic segmentation network is proposed to increase the sample diversity and enlarge the data set. Then, a dynamic sample updating strategy is put forward to cover more welding situations. Finally, faced with the weak-feature and weak-texture characteristics of weld beads, a simplified YOLOV3 model is proposed to realize end-to-end weld bead location. Experiments demonstrate that the proposed method could effectively satisfy the robustness and precision requirements for weld bead detection and location combined with a deep semantic segmentation network and simplified YOLOV3 model.
Year
DOI
Venue
2021
10.1109/TIM.2020.3026514
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Keywords
DocType
Volume
Data augmentation, deep convolutional network model, object location, samples updating, semantic segmentation, weld bead
Journal
70
ISSN
Citations 
PageRank 
0018-9456
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Lei Yang110.68
Junfeng Fan233429.09
Yanhong Liu33010.15
En Li414813.04
Jinzhu Peng5164.78
Zi-ze Liang610619.61