Automatic image thresholding using Otsu’s method and entropy weighting scheme for surface defect detection

Nhat, Truong Thanh; Kim, Sang-Hoon

doi:10.1007/s00500-017-2709-1

Cited by 58 publications

(44 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the boundary is preserved, while the noise is reduced. Equation (1) gives the specific operation of bilateral filtering:…”

Section: Filtersmentioning

confidence: 99%

“…With the rapid expansion of network applications, computer vision technology has been successfully applied to the quality inspection of industrial production [1][2][3][4][5][6], including glass products [1], fabrics [2,3], steel surfaces [4], bearing rollers [5], and casting surfaces [6]. The inspection of these mentioned examples needs a matching algorithm to extract image features based on the actual defect situation.…”

Section: Introductionmentioning

confidence: 99%

“…The inspection of these mentioned examples needs a matching algorithm to extract image features based on the actual defect situation. For glass products for packaging and domestic use, an intelligent system based on the classical computer vision technology was proposed for the automatic inspection of two types of defects [1]. For fabric quality control, an unsupervised learning-based automated approach to verify and localize fabric defects was proposed [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

et al. 2019

View full text Add to dashboard Cite

Automatic vision inspection technology shows a high potential for quality inspection, and has drawn great interest in micro-armature manufacturing. Given that the inspection process is highly influenced by the lack of real standardization and efficiency performed with the human eye, thus, it is necessary to develop an automatic defect detection process. In this work, an elaborated vision system for the defect inspection of micro-armatures used in smartphones was developed. It consists of two parts, the front-end module and the deep convolution neural networks (DCNNs) module, which are responsible for different areas. The front-end module runs first and the DCNNs module will not run if the output of the front-end module is negative. To verify the application of this system, an apparatus consisting of an objective table, control panel, and a camera connected to a Personal Computer (PC) was used to simulate an industrial position of production. The results indicate that the developed vision system is capable of defect detection of micro-armatures.Recently, deep convolutional neural networks (DCNNs) have been proved to be important methods in visual detection. However, some classical methods should still be considered. Literature [6] mentioned above is an example. For instance, in order to get better parameters, a suitable smart manufacturing strategy for real industrial conditions was proposed. The results of this dataset showed that the Adaboost ensembles provided the highest accuracy and were more easily optimized than ANNs [7]. Obviously, for now, there are some limitations with using classical methods alone or using DCNNs directly. For classical methods, for example, they usually come with high complexity of programming and less tolerance to data variability. With regard to the DCNNs, a large number of samples are needed for network training and the iterative optimization of parameters is partly a black-box operation [8].In the industrial applications, part defects in the samples can be easily identified with a picture by the classical computer vision method. Therefore, we do not need to feed the whole picture into the network for discrimination, which can reduce the number of features to be identified. As a result, the network is easier to converge. Therefore, by combining the classical image recognition method and DCNNs, the landing speed of deep learning technology in industrial applications can be effectively improved.In this work, an intelligent detection system that combined the classical computer vision method and DCNNs was designed to automatically detect the quality defects of micro-motor armatures. Firstly, the quality, excluding the region of copper wire crossing (ROC), is decided based on the classical computer vision technology. If the first result is positive, the ROC will be extracted and sent to the DCNNs for identification. If the result is still positive, the image is a defect-free sample, otherwise the whole image is labeled defective. In the experiments, this system works very fast and presen...

show abstract

“…Thus, the boundary is preserved, while the noise is reduced. Equation (1) gives the specific operation of bilateral filtering:…”

Section: Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Because of the strong noise and vibration of the coal mining face, the predicted distribution of coal and rock is not accurate and the practical application effect based on above methods is unsatisfactory and unacceptable. Image processing methods have been widely used in many fields, such as face recognition, video surveillance analysis, intelligent driving, industrial visual inspection, and text recognition [4][5][6][7][8][9][10]. In the coal mining face, the monitoring images of the coal seam contain abundant coal-rock feature information.…”

Section: Introductionmentioning

confidence: 99%

A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face

Xiong

Wang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Accurate identification of the distribution of coal seam is a prerequisite for realizing intelligent mining of shearer. This paper presents a novel method for identifying coal and rock based on a deep convolutional neural network (CNN). Three regularization methods are introduced in this paper to solve the overfitting problem of CNN and speed up the convergence: dropout, weight regularization, and batch normalization. Then the coal-rock image information is enriched by means of data augmentation, which significantly improves the performance. The shearer cutting coal-rock experiment system is designed to collect more real coal-rock images, and some experiments are provided. The experiment results indicate that the network we designed has better performance in identifying the coal-rock images.

show abstract

“…For these purposes the comprehensively practiced technique is automatic thresholding. In automatic thresholding [1] the value of best possible gray-level threshold is preferred on the way to take apart objects out of the background image according to their intensity distribution. In applications of identifying defects, possess dissimilar patterns and dimensions, which range in exceedingly minute to inordinate.…”

Section: Introductionmentioning

confidence: 99%

Metallic Surface Coating Defect Detection Using Firefly Based Adaptive Thresholding and Level Set Method

Aslam¹,

Santhi²,

Ramasamy³

et al. 2019

IJITEE

View full text Add to dashboard Cite

An innovative approach is introduced to detect surface defects on titanium coated steel surfaces with varied size through the use of image processing techniques. This paper provides techniques which are useful to discover numerous kinds of surface defects present on coating surface. For defect detection, Firefly Algorithm (FA) based adaptive thresholding is proposed and is applied for the gray scale images. The FA ensuing nature inspired algorithm utilized expansively in support of determining various optimization problems and from the reconstructed image contours are extracted using level set method, the predictable images not including textures besides defects contours be compassed. The morphological post processing removes the noise in image and makes defects more distinguishable from the background. The speculative result persists in utilizing synchronous images of metal surface and shows that the proposed method can efficiently segment surface defects and obtain better performance than existing methods.

show abstract

Automatic image thresholding using Otsu’s method and entropy weighting scheme for surface defect detection

Cited by 58 publications

References 14 publications

An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones

A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face

Metallic Surface Coating Defect Detection Using Firefly Based Adaptive Thresholding and Level Set Method

Contact Info

Product

Resources

About