Weld defect classification based on texture features and principal component analysis

Jiang, Hongquan; Zhao, Yuwu; Gao, Jianmin; Wang, Zhao

doi:10.1784/insi.2016.58.4.194

Cited by 19 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These filters include mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation. To reduce high dimensional and redundant texture features, Principal Component Analysis (PCA) is carried out to achieve a more accurate classification result by [48]. As shown in Table 6, a total of 36 features derived from Sentinel-2A and DEM imagery was chosen to extract the forested areas, including 21 spectral features, 12 textural features and three topographic features.…”

Section: Forestland Classification At Level-2mentioning

confidence: 99%

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

et al. 2018

View full text Add to dashboard Cite

Abstract:Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales.

show abstract

Section: Forestland Classification At Level-2mentioning

confidence: 99%

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Gray level cooccurrence matrix (GLCM) [18] method was developed based on the cooccurrence statistics. Jiang et al [19] performed weld defect classification using GLCM. Approaches using filters such as the Gabor filter were widely used for texture representation in the early years [8].…”

Section: Related Workmentioning

confidence: 99%

Effective Defect Detection Method Based on Bilinear Texture Features for LGPs

Hong¹,

Zhou

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Automatic defect detection of light guide plates (LGPs) is an important task in the manufacture of liquid crystal displays. During thermo-printing, defects of tag lines on LGPs may occur easily, and these defects are of two categories: bubbles and missing tag lines. These defects lack salient visual attributes, such as edge-based and region-based features, and as such, traditional methods fail to detect them. To address this, we propose a Dense-bilinear convolutional neural network (BCNN), an endto-end defect detection network, utilizing Dense-blocks [1], Bilinear feature layers [2], and squeeze-andexcitation blocks [3]. Our network exploits fine-grained texture features, which leads to parameter reduction and accuracy enhancement. We validate our network on our LGP dataset containing 5,860 images from three cases: bubbles, tag line existence, and tag line missing. Our network outperforms AlexNet [4], VGG [5], and ResNet [6] on both the public and our LGP datasets with less GPU memory consumption.

show abstract

“…In Reference [6], texture and geometrical features need to be artificially described. In References [11,12], all images need to be vectorized in advance, and the covariance matrix dimension after the vector is too large, which affects the feature extraction speed. In our feature extraction module, the CNN automatically learns high-level features from the images.…”

Section: Evaluation Of Feature Extraction Modulementioning

confidence: 99%

“…Zahran et al [10] extracted feature from the power density spectra (PDS) of the weld segmented areas and used ANN to match features in order to recognize different defects. In References [11,12], a method based on principal component analysis (PCA) and SVM was proposed. It can effectively transform weld defects images to principal component space through PCA and complete the classification using SVM.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Classification of Weld Surface Defects

Zhu

Liu

2019

Applied Sciences

View full text Add to dashboard Cite

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

show abstract

Weld defect classification based on texture features and principal component analysis

Cited by 19 publications

References 0 publications

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

Effective Defect Detection Method Based on Bilinear Texture Features for LGPs

Deep Learning-Based Classification of Weld Surface Defects

Contact Info

Product

Resources

About