Research on On-Line Surface Defect Detection for Steel Strip Based on Sparse Coding

2012 ◽  
Vol 548 ◽  
pp. 749-752 ◽  
Author(s):  
Zhao Liu ◽  
Jia Hu ◽  
Li Hu ◽  
Xiao Long Zhang ◽  
Jian Yi Kong
Keyword(s):  
Machine Vision ◽  
Sparse Coding ◽  
Visual Information ◽  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Feature Representation ◽  
Surface Defect Detection ◽  
On Line ◽  
Object Feature

In the field of metallurgy, surface defects detection for steel plate based on machine vision is a new key technology. In order to improve the accuracy and speed of machine vision in real-time surface defects detection, taking into account the neurons selectivity and sparseness to visual information, we present a flexible data selection mechanism in the layer of photoreceptors and a new sparse coding model for object feature representation and object recognition. Experiments show that the new method is more effective and more effective in the process of training and classification. The key finding of this study is that, the effective sparse coding mechanism not only could have occurred in the data input stage, but also could be in a new way.

Surface Defect Detection of Chinese Dates Based on Machine Vision

2011 ◽  
Vol 403-408 ◽  
pp. 1356-1359
Author(s):  
Fu Juan Wang ◽  
Yong Qiang Dong
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Surface Defect ◽  
Color Space ◽  
Defect Area ◽  
Surface Defect Detection ◽  
On Line ◽  
Rgb Color Space ◽  
Image Thinning ◽  
Chinese Date

In order to implement the accuracy and robust of Chinese dates surface defect detection based on machine vision techniques on line, the method of detection for Chinese dates was studied. The Chinese date is segmented from the background in RGB color space by analyzing respectively the histogram of R, G and B channel to make comparing among them and find an optimal one, resulting in good contrast between Chinese date and background in G channel. The brightness of the damaged area edge changed clearly on the whole Chinese dates area according to the gray image of R, G and B channel, especially in G channel. It shows the gray value of the defect area breaking obviously. So the damaged area could be detected by edge detect, through image thinning the defect edge was extracted. Furthermore, the geometry parameters of defect edge were calculated, these parameters could used to distinguish the defect area with the fruit area and the degree of the defect area. Experiments result proved the methods is effective to detect defect area of Chinese date.

scholarly journals Segmented Embedded Rapid Defect Detection Method for Bearing Surface Defects

Machines ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Linjian Lei ◽  
Shengli Sun ◽  
Yue Zhang ◽  
Huikai Liu ◽  
Hui Xie
Keyword(s):  
Machine Vision ◽  
Defect Detection ◽  
Character Recognition ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Rapid Development ◽  
Detection Accuracy ◽  
Bearing Surface ◽  
Surface Defect Detection

The rapid development of machine vision has prompted the continuous emergence of new detection systems and algorithms in surface defect detection. However, most of the existing methods establish their systems with few comparisons and verifications, and the methods described still have various problems. Thus, an original defect detection method: Segmented Embedded Rapid Defect Detection Method for Surface Defects (SERDD) is proposed in this paper. This method realizes the two-way fusion of image processing and defect detection, which can efficiently and accurately detect surface defects such as depression, scratches, notches, oil, shallow characters, abnormal dimensions, etc. Besides, the character recognition method based on Spatial Pyramid Character Proportion Matching (SPCPM) is used to identify the engraved characters on the bearing dust cover. Moreover, the problem of characters being cut in coordinate transformation is solved through Image Self-Stitching-and-Cropping (ISSC). This paper adopts adequate real image data to verify and compare the methods and proves the effectiveness and advancement through detection accuracy, missing alarm rate, and false alarm rate. This method can provide machine vision technical support for bearing surface defect detection in its real sense.

scholarly journals A Study on Railway Surface Defects Detection Based on Machine Vision

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1437
Author(s):  
Tangbo Bai ◽  
Jialin Gao ◽  
Jianwei Yang ◽  
Dechen Yao
Keyword(s):  
Machine Vision ◽  
Surface Defect ◽  
Surface Defects ◽  
Image Features ◽  
Detection Accuracy ◽  
Test Results ◽  
Defect Area ◽  
Surface Defect Detection ◽  
Speed Up ◽  
Track Surface

The detection of rail surface defects is an important tool to ensure the safe operation of rail transit. Due to the complex diversity of track surface defect features and the small size of the defect area, it is difficult to obtain satisfying detection results by traditional machine vision methods. The existing deep learning-based methods have the problems of large model sizes, excessive parameters, low accuracy and slow speed. Therefore, this paper proposes a new method based on an improved YOLOv4 (You Only Look Once, YOLO) for railway surface defect detection. In this method, MobileNetv3 is used as the backbone network of YOLOv4 to extract image features, and at the same time, deep separable convolution is applied on the PANet layer in YOLOv4, which realizes the lightweight network and real-time detection of the railway surface. The test results show that, compared with YOLOv4, the study can reduce the amount of the parameters by 78.04%, speed up the detection by 10.36 frames per second and decrease the model volume by 78%. Compared with other methods, the proposed method can achieve a higher detection accuracy, making it suitable for the fast and accurate detection of railway surface defects.

scholarly journals X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 706
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Rolled Strip ◽  
Rolled Steel ◽  
Strip Surface ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Hot Rolled Steel ◽  
Detection Of Defects

It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

scholarly journals An improved Vision Transformer-based method for classifying surface defects in hot-rolled strip steel

2021 ◽  
Vol 2082 (1) ◽  
pp. 012016
Author(s):  
Xinglong Feng ◽  
Xianwen Gao ◽  
Ling Luo
Keyword(s):  
Surface Defect ◽  
Surface Defects ◽  
Steel Strip ◽  
Learning Ability ◽  
Rolled Strip ◽  
Strip Surface ◽  
Strip Steel ◽  
Hot Rolled Strip ◽  
Hot Rolled ◽  
Improved Algorithm

Abstract A new Vision Transformer(ViT) model is proposed for the classification of surface defects in hot rolled strip, optimizing the poor learning ability of the original Vision Transformer model on smaller datasets. Firstly, each module of ViT and its characteristics are analyzed; Secondly, inspired by the deep learning model VGGNet, the multilayer fully connected layer in VGGNet is introduced into the ViT model to increase its learning capability; Finally, by performing on the X-SDD hot-rolled steel strip surface defect dataset. The effect of the improved algorithm is verified by comparison experiments on the X-SDD hot-rolled strip steel surface defect dataset. The test results show that the improved algorithm achieves better results than the original model in terms of accuracy, recall, F1 score, etc. Among them, the accuracy of the improved algorithm on the test set is 5.64% higher than ViT-Base and 2.64% higher than ViT-Huge; the accuracy is 4.68% and 1.36% higher than both of them, respectively.

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