Log in

Relevant bibliographies by topics / Image defects / Journal articles

To see the other types of publications on this topic, follow the link: Image defects.

Journal articles on the topic 'Image defects'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Image defects.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Lee, Wei-Chen, and Pei-Ling Tai. "Defect Detection in Striped Images Using a One-Dimensional Median Filter." Applied Sciences 10, no. 3 (2020): 1012. http://dx.doi.org/10.3390/app10031012.

Full text

Abstract:

Defect detection is a key element of quality assurance in many modern manufacturing processes. Defect detection methods, however, often involve a great deal of time and manual work. Image processing has become widely used as a means of reducing the required detection time and effort in manufacturing. To this end, this study proposes an image-processing algorithm for detecting defects in images with striped backgrounds—defect types include scratches and stains. In order to detect defects, the proposed method first pre-processes images and rotates them to align the stripes horizontally. Then, th

APA, Harvard, Vancouver, ISO, and other styles

2

Zhang, Na, Gang Yang, Fan Hu, Hua Yu, Jingjing Fan, and Siqing Xu. "A Novel Adversarial Deep Learning Method for Substation Defect Image Generation." Sensors 24, no. 14 (2024): 4512. http://dx.doi.org/10.3390/s24144512.

Full text

Abstract:

The presence of defects in substation equipment is a major factor affecting the safety of power transmission. Therefore, timely and accurate detection of these defects is crucial. As intelligent inspection robots advance, using mainstream object detection models to diagnose surface defects in substation equipment has become a focal point of current research. However, the lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. To address the issue of insufficient training data for defect images with complex backgroun

APA, Harvard, Vancouver, ISO, and other styles

3

Chen, Ming Fu, Chih Chung Chou, Chun Jie Lien, and Rui Cian Weng. "An Optical Inspection System for Chip Defects Using a High Rate Line Scanner." Applied Mechanics and Materials 574 (July 2014): 462–67. http://dx.doi.org/10.4028/www.scientific.net/amm.574.462.

Full text

Abstract:

Based on a line scan imager, we propose anew defect inspection system to precisely identify defects with size of greater than criteria from chip backside. Only a corresponding raw image is acquiredfor each chip,and using anopto-mechanical device with very low geometry distortion to make it feasible to neglectprocesses of image calibration and mosaic. Defect inspection is based on a binary chip edge image and a novel methodof using edge pixels statistic. Thus defect size of crack, chipping and glue can be inspected quantitatively and efficiently.From test results by integrating inspection syste

APA, Harvard, Vancouver, ISO, and other styles

4

Dr. Avinash S, Dr. Prathapchandra, and Mr. Ramachandra. "Fault Detection in Printed Circuit Board (PCB) using Image Subtraction Method." International Journal of Innovative Technology and Exploring Engineering 13, no. 7 (2024): 1–6. http://dx.doi.org/10.35940/ijitee.g9906.13070624.

Full text

Abstract:

Fault detection in PCBs is a important task in the electronics industry to ensure the consistency and performance of electronic devices. One common approach for detecting defects in PCBs is the subtraction method, which involves subtracting a reference image of a defect-free PCB from an image of a PCB with defects. The resulting image gives the differences between the two images, making it easier to detect and classify defects. In this work, a defect detection system for PCBs using the subtraction method using MATLAB is proposed. The research work uses publicly available PCB defect datasets to

APA, Harvard, Vancouver, ISO, and other styles

5

Dr., Avinash S. "Fault Detection in Printed Circuit Board (PCB) using Image Subtraction Method." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 7 (2024): 1–6. https://doi.org/10.35940/ijitee.G9906.13070624.

Full text

Abstract:

<strong>Abstract: </strong>Fault detection in PCBs is a important task in the electronics industry to ensure the consistency and performance of electronic devices. One common approach for detecting defects in PCBs is the subtraction method, which involves subtracting a reference image of a defect- free PCB from an image of a PCB with defects. The resulting image gives the differences between the two images, making it easier to detect and classify defects. In this work, a defect detection system for PCBs using the subtraction method using MATLAB is proposed. The research work uses publicly avai

APA, Harvard, Vancouver, ISO, and other styles

6

Zhou, Yang, Lijuan Zhu, and Dong Ma. "Research on Traditional Image Segmentation Method Based on Oil Drilling Pipe Defects." Journal of Physics: Conference Series 2639, no. 1 (2023): 012057. http://dx.doi.org/10.1088/1742-6596/2639/1/012057.

Full text

Abstract:

Abstract This study explores the feasibility and efficacy of conventional image seg-mentation technology in diagnosing failures in oil drilling pipe images. Simultaneously, it envisions an intelligent approach to diagnose defects in oil drilling pipes. The present paper examines and scrutinizes traditional image segmentation methods in light of the characteristics of oil drilling pipe defect images. It devises experiments tailored for these defect images and employs various traditional image segmentation methods to facilitate comparison and evaluation. The experimental findings illustrate that

APA, Harvard, Vancouver, ISO, and other styles

7

Guo, LongYuan, ShiNan Li, Wenjing Hu, et al. "Sub-Pixel Level Defect Detection Based on Notch Filter and Image Registration." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 06 (2018): 1854016. http://dx.doi.org/10.1142/s0218001418540162.

Full text

Abstract:

General machine vision algorithms are difficult to detect LCD sub-pixel level defects. By studying the LCD screen images, we found that the pixels in the LCD screen are regularly arranged. The spectrum distribution of LCD images, which is obtained by the Fourier transform, is relatively consistent. According to this feature, a method of sub-pixel defect detection based on notch filter and image registration is proposed. First, we take a defect-free template image to establish registration template and notch-filtering template; then we take the defect images for image registration with registra

APA, Harvard, Vancouver, ISO, and other styles

8

Gao, Pengyu, Xin Yan, Jinpeng He, Haojun Yang, Xindu Chen, and Xiangdong Gao. "Processing of Eddy Current Infrared Thermography and Magneto-Optical Imaging for Detecting Laser Welding Defects." Metals 15, no. 2 (2025): 119. https://doi.org/10.3390/met15020119.

Full text

Abstract:

Infrared (IR) magneto-optical (MO) bi-imaging is an innovative method for detecting weld defects, and it is important to process both IR thermography and MO imaging characteristics of weld defects. IR thermography and MO imaging can not only run simultaneously but can also run separately in special welding processes. This paper studies the sensing processing of eddy current IR thermography and MO imaging for detecting weld defects of laser spot welding and butt joint laser welding, respectively. To address the issues of high-level noise and low contrast in eddy current IR detection thermal ima

APA, Harvard, Vancouver, ISO, and other styles

9

Wei, Bing, Lei Gao, Xue-song Tang, and Kuangrong Hao. "Multi-Class Object Learning with Application to Fabric Defects Detection." AATCC Journal of Research 8, no. 1_suppl (2021): 165–72. http://dx.doi.org/10.14504/ajr.8.s1.20.

Full text

Abstract:

Deep convolutional neural networks (CNNs) have shown great success in single-class fabric image detection. However, real-world fabric defect images generally contain several types of defects in one image. Accurately recognizing and classifying multi-class fabric defect images is still an unsolved issue due to the complexity of intersected defects, as well as difficulty in distinguishing small-size defects. To address these challenges, this study develops a methodology based on the deep learning feature pyramid networks (FPN) approach to detect multi-class fabric defects. To evaluate the propos

APA, Harvard, Vancouver, ISO, and other styles

10

Riza, Dimas Firmanda Al, Tetsuhito Suzuki, Yuichi Ogawa, and Naoshi Kondo. "External Defects and Soil Deposits Identification on Potato Tubers using 2CCD Camera and Principal Component Images." Industria: Jurnal Teknologi dan Manajemen Agroindustri 12, no. 2 (2023): 143–55. http://dx.doi.org/10.21776/ub.industria.2023.012.02.4.

Full text

Abstract:

Precise recognition of potato external defects and the ability to identify defects and non-defect areas are in demand. Common scab represents a significant issue that requires detection, yet identifying the extent of common scab infection remains challenging when using a standard RGB camera. In this research, a 2CCD camera system that could obtain a set of RGB and near-infrared images, which could enhance defect detection, has been used. Image segmentation strategies based on a single principal component image and the principal component pseudo-colored image have been proposed to identify exte

APA, Harvard, Vancouver, ISO, and other styles

11

He, Min, Qinnan Zhao, Huanhuan Gao, Xinying Zhang, and Qin Zhao. "Image Segmentation of a Sewer Based on Deep Learning." Sustainability 14, no. 11 (2022): 6634. http://dx.doi.org/10.3390/su14116634.

Full text

Abstract:

An accurate assessment of the type and extent of sewer damage is an important prerequisite for maintenance and repair. At present, distinguishing drainage pipe defect types in the engineering field mainly relies on the human eye, which is time consuming, labor intensive, and subjective. Some studies have used deep learning to classify the types of pipe defects, but this method can only identify one main pipe defect. However, sometimes a combination of defects, such as corrosion and precipitation on a section of pipe wall, can be classified as one category by picture classification, which is si

APA, Harvard, Vancouver, ISO, and other styles

12

Mu, Hong Bo, Da Wei Qi, and Ming Ming Zhang. "Image Segmentation of Wood with Knot Defects Based on Gray Transformation." Applied Mechanics and Materials 71-78 (July 2011): 1691–94. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.1691.

Full text

Abstract:

Wood knot image obtained by X-ray was done gray transformation. The contrast of the image after gray transformation can be enhanced obviously and the position of knot can be highlighted. Binary processing was adopted for the image after gray transformation. The defects areas of the binary images are filled. Then, invert the best binary image of wood defect, and then adduct the obtained image. The result of wood defect image plus is that defects regions take apart completely with their background, which means the image segmentation is completed. Wood utilization is improved. The experiment resu

APA, Harvard, Vancouver, ISO, and other styles

13

Tao, Yi. "Research on metal image defect detection based on convolutional neural network, random forest model and support vector machine model." Applied and Computational Engineering 45, no. 1 (2024): 312–18. http://dx.doi.org/10.54254/2755-2721/45/20241670.

Full text

Abstract:

Metal image defect detection is a very important task, which can help manufacturers to detect defects in metal products in time during the production process, so as to improve product quality and production efficiency. This paper presents a private dataset containing images of six different types of metal surface defects, including scratches, pits, indentations, bubbles, oil stains, and aluminum surface defects. The dataset contains 1800 images, 300 images for each defect type, each with a size of 200x200 pixels. The dataset is divided into training, validation and test sets in the ratio of 6:

APA, Harvard, Vancouver, ISO, and other styles

14

Ben Gharsallah, Mohamed, and Ezzeddine Ben Braiek. "Weld Inspection Based on Radiography Image Segmentation with Level Set Active Contour Guided Off-Center Saliency Map." Advances in Materials Science and Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/871602.

Full text

Abstract:

Radiography is one of the most used techniques in weld defect inspection. Weld defect detection becomes a complex task when uneven illumination and low contrast characterize radiographic images. In this paper we propose a new active contour based level set method for weld defect detection in radiography images. An off-center saliency map exploited as a feature to represent image pixels is embedded into a region energy minimization function to guide the level set active contour to defects boundaries. The aim behind using salient feature is that a small defect can frequently attract attention of

APA, Harvard, Vancouver, ISO, and other styles

15

Chen, Liang Chia, Chia Cheng Kuo, and Ping Ang Yen. "Automatic Optical Inspection on TFT-LCD Mura Defects Using Background Image Reconstruction." Key Engineering Materials 364-366 (December 2007): 400–403. http://dx.doi.org/10.4028/www.scientific.net/kem.364-366.400.

Full text

Abstract:

A mura defect detection algorithm for thin-film transistor liquid crystal display (TFTLCD) is developed for automatic detection of mura defects using Discrete Cosine Transform (DCT) principle for background image reconstruction. Detecting blob-mura defects in a LCD panel can be difficult due to non-uniform brightness background and slightly different brightness levels between the defect region and the background. To resolve this issue, a DCT-based background reconstruction algorithm was developed to establish the background image. The background of the inspected images can be first extracted a

APA, Harvard, Vancouver, ISO, and other styles

16

Wang, Sicheng, Bin Yan, Xinyue Xu, et al. "Automated Identification and Localization of Rail Internal Defects Based on Object Detection Networks." Applied Sciences 14, no. 2 (2024): 805. http://dx.doi.org/10.3390/app14020805.

Full text

Abstract:

The timely identification of rail internal defects and the application of corresponding preventive measures would greatly reduce catastrophic failures, such as rail breakage. Ultrasonic rail defect detection is the current mainstream rail defect detection method thanks to its advantages of strong penetration, high accuracy, and ease to deploy. The 2D B-scan image output by ultrasonic detectors contains rich features of defects; however, rail engineers manually identify and localize the defect image, which can be time-consuming, and the image may be subject to missing identification or mistakes

APA, Harvard, Vancouver, ISO, and other styles

17

Jia, Feixiang, Yayu Chen, and Wei Hao. "Research on Defect Detection of Bare Film in Landfills Based on a Temperature Spectrum Model." Applied Sciences 15, no. 9 (2025): 4774. https://doi.org/10.3390/app15094774.

Full text

Abstract:

Due to the construction damage of high-density polyethylene film (HDPE) during the early stages of landfill construction and missed or faulty welding, this paper proposes a method based on the synchronous characteristic temperature differences between defective and intact areas of HDPE film. An image feature-edge-picking algorithm was used to detect various defects. First, under the action of a continuous heat source, infrared images of different types of defects on the surface of HDPE films were collected, and we recorded the temperature of different areas on the film surface. We also analyze

APA, Harvard, Vancouver, ISO, and other styles

18

Li, Jiaming, Xiaoxun Zhang, Fang Ma, Shuxian Wang, and Yuanyou Huang. "Simultaneous Pore Detection and Morphological Features Extraction in Laser Powder Bed Fusion with Image Processing." Materials 17, no. 6 (2024): 1373. http://dx.doi.org/10.3390/ma17061373.

Full text

Abstract:

Internal pore defects are inevitable during laser powder bed fusion (LPBF), which have a significant impact on the mechanical properties of the parts. Therefore, detecting pores and obtaining their morphology will contribute to the quality of LPBF parts. Currently, supervised models are used for defect image detection, which requires a large amount of LPBF sample data, image labeling, and computing power equipment during the training process, resulting in high detection costs. This study extensively collected LPBF sample data and proposed a method for pore defect classification by obtaining it

APA, Harvard, Vancouver, ISO, and other styles

19

Patel, Jagrti, Meghna Jain, and Papiya Dutta. "Detection of Faults Using Digital Image Processing Technique." Asian Journal of Engineering and Applied Technology 2, no. 1 (2013): 36–39. http://dx.doi.org/10.51983/ajeat-2013.2.1.644.

Full text

Abstract:

This paper presents an approach to automatic detection of fabric defects using digital image processing. In Textile industry automatic fabric inspection is important to maintain the quality of fabric. Fabric defect detection is carried out manually with human visual inspection for a long time. This paper proposes an approach to recognize fabric defects in textile industry for minimizing production cost and time. Fabric analysis is performed on the basis of digital images of the fabric. The recognizer acquires digital fabric images by image acquisition device and converts that image into binary

APA, Harvard, Vancouver, ISO, and other styles

20

Lin, Keh-Moh, Horng-Horng Lin, Harshad Kumar Dandage, and Yi-Chun Du. "Pseudo colorization of electroluminescence images of multi-crystalline silicon solar cells for defect inspection." Modern Physics Letters B 33, no. 14n15 (2019): 1940010. http://dx.doi.org/10.1142/s0217984919400104.

Full text

Abstract:

Computer imaging of electroluminescence (EL) has been successfully applied to solar cell inspection in recent years, as EL image intensities reflect the efficiency levels and/or defects in sc-Si and mc-Si solar cells. In this paper, we propose a novel computational scheme for pseudo colorization of EL images to highlight defect regions in solar cells for human inspection. Specifically, given a template EL image and pseudo color labels on its defect regions, we impose the pseudo colors to other grayscale EL images, with respect to different defect types and image structures, by template feature

APA, Harvard, Vancouver, ISO, and other styles

21

Pan, Qingtian, Miao Chen, Baoqi Zuo, and Yucai Hu. "The Inspection of Raw- Silk Defects Using Image Vision." Journal of Engineered Fibers and Fabrics 13, no. 3 (2018): 155892501801300. http://dx.doi.org/10.1177/155892501801300309.

Full text

Abstract:

The inspection of defects is one of the most important aspects in the quality inspection of raw silk. We introduce a raw-silk defect detection system based on image vision and image analysis that is accurate and objective. In the experimental phase, we develop an image-acquisition section—which includes a charge-coupled device (CCD) image sensor, a telecentric lens, a light source, and a raw-silk winding device to capture the raw silk images steadily. After the image capture stage, an image-processing section tasked with threshold segmentation and morphology operations is carried out to obtain

APA, Harvard, Vancouver, ISO, and other styles

22

Si, Xiao Shu, Hong Zheng, and Xue Min Hu. "Fabric Defect Detection Based on SRG-PCNN." Advanced Materials Research 148-149 (October 2010): 1319–26. http://dx.doi.org/10.4028/www.scientific.net/amr.148-149.1319.

Full text

Abstract:

Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN i

APA, Harvard, Vancouver, ISO, and other styles

23

Wang, Jinmian, Jiping Qiao, and Meicen Guo. "Research on bearing surface defect detection system based on machine vision." Journal of Physics: Conference Series 2290, no. 1 (2022): 012061. http://dx.doi.org/10.1088/1742-6596/2290/1/012061.

Full text

Abstract:

Abstract Aiming at the shortcomings of manual detection methods for surface defects of bearing outer rings, a surface defect detection method based on machine vision is proposed. The common defects on the bearing surface are scratches, cracks, and peeling. The defect images are analyzed by the LabVIEW image processing module. Make fast and accurate inspections. In this paper, a CMOS industrial camera is used to obtain the bearing image, and the gray histogram of the image is analyzed to determine whether it is a defective bearing. In order to solve the problem of uneven illumination in image s

APA, Harvard, Vancouver, ISO, and other styles

24

Yixuan, Liu, Wu Dongbo, Liang Jiawei, and Wang Hui. "Aeroengine Blade Surface Defect Detection System Based on Improved Faster RCNN." International Journal of Intelligent Systems 2023 (May 10, 2023): 1–14. http://dx.doi.org/10.1155/2023/1992415.

Full text

Abstract:

Aiming at the difficulty of automatic blade detection and the discontinuous defects on the full image, an aeroengine blade surface defect detection system based on improved faster RCNN is designed. Firstly, a dataset of blade surface defects is constructed. To solve the problem that the original faster RCNN is hard to detect tiny defects, RoI align is adopted to replace RoI pooling in the improved faster RCNN and the feature pyramid networks (FPN) combined with ResNet-50 are introduced for feature extraction. To address the issue of discontinuous defects on the full image, the nonmaximum suppr

APA, Harvard, Vancouver, ISO, and other styles

25

Deng, Shuixin, Lei Deng, Ting Sun, et al. "EEDD: Edge-Guided Energy-Based PCB Defect Detection." Electronics 12, no. 10 (2023): 2306. http://dx.doi.org/10.3390/electronics12102306.

Full text

Abstract:

Printed circuit board (PCB) defect detection is critical for ensuring the safety of electronic devices, especially in the space industry. Non-reference-based methods, typically the deep learning methods, suffer from a large amount of annotated data requirements and poor interpretability. In contrast, conventional reference-based methods achieve higher detection accuracy by comparing with a template image but rely on precise image alignment and face the challenge of fine defects detection. To solve the problem, we propose a novel Edge-guided Energy-based PCB Defect Detection method (EEDD). We f

APA, Harvard, Vancouver, ISO, and other styles

26

Sioma, Andrzej. "Automated Control of Surface Defects on Ceramic Tiles Using 3D Image Analysis." Materials 13, no. 5 (2020): 1250. http://dx.doi.org/10.3390/ma13051250.

Full text

Abstract:

This paper presents a method of acquisition and analysis of three-dimensional images in the task of automatic location and evaluation of defects on the surface of ceramic tiles. It presents a brief description of selected defects appearing on the surface of tiles, along with the analysis of their formation. The paper includes the presentation of the method of constructing a 3D image of the tile surface using the Laser Triangulation Method (LTM), along with the surface imaging parameters employed in the research. The algorithms of three-dimensional surface image analysis of ceramic tiles used i

APA, Harvard, Vancouver, ISO, and other styles

27

Jiang, Zhaolin, Xueyuan Hu, and Sunxin Wang. "Image Classification of Car Paint Defect Detection Based on Convolutional Neural Networks." Journal of Physics: Conference Series 2456, no. 1 (2023): 012037. http://dx.doi.org/10.1088/1742-6596/2456/1/012037.

Full text

Abstract:

Abstract In the study of using images to display car paint defects, the current need is to use deep Convolutional Neural Networks (CNN) to identify and classify different types of car paint defects, so as to give full play to the application of image processing in the field of automatic car paint defect detection. Using the collected car paint defect images, the car paint defects dataset is established. The preprocessing process of original data and the application of three image classification models based on CNN are visually displayed. First, the dataset of 7 types of car body defects includ

APA, Harvard, Vancouver, ISO, and other styles

28

Xia, Yuwei, Sang Wook Han, and Hyock Ju Kwon. "Image Generation and Recognition for Railway Surface Defect Detection." Sensors 23, no. 10 (2023): 4793. http://dx.doi.org/10.3390/s23104793.

Full text

Abstract:

Railway defects can result in substantial economic and human losses. Among all defects, surface defects are the most common and prominent type, and various optical-based non-destructive testing (NDT) methods have been employed to detect them. In NDT, reliable and accurate interpretation of test data is vital for effective defect detection. Among the many sources of errors, human errors are the most unpredictable and frequent. Artificial intelligence (AI) has the potential to address this challenge; however, the lack of sufficient railway images with diverse types of defects is the major obstac

APA, Harvard, Vancouver, ISO, and other styles

29

Chen, Heng, Teng Fei Lei, Ying Liu, and Xu Wang. "Study of Infrared Thermograph NDT of Airplane Composites." Applied Mechanics and Materials 543-547 (March 2014): 1018–21. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1018.

Full text

Abstract:

In this paper, infrared thermal wave NDT techniques are used for aircraft composites. After the infrared image is sharpened and the temperature field is analyzed, aircraft composite material defects can be diagnosed. Experiments show that the infrared NDT techniques can detect defects in aircraft composite image. Images and first-order differential image can be presented more clearly outline the process and temperature variations. Defect size and depth can also be measured. The best experiments are designed for different materials. After several experiments of the precise temperature calibrati

APA, Harvard, Vancouver, ISO, and other styles

30

Yun, Gwang-ho, Sang-jin Oh, and Sung-chul Shin. "Image Preprocessing Method in Radiographic Inspection for Automatic Detection of Ship Welding Defects." Applied Sciences 12, no. 1 (2021): 123. http://dx.doi.org/10.3390/app12010123.

Full text

Abstract:

Welding defects must be inspected to verify that the welds meet the requirements of ship welded joints, and in welding defect inspection, among nondestructive inspections, radiographic inspection is widely applied during the production process. To perform nondestructive inspection, the completed weldment must be transported to the nondestructive inspection station, which is expensive; consequently, automation of welding defect detection is required. Recently, at several processing sites of companies, continuous attempts are being made to combine deep learning to detect defects more accurately.

APA, Harvard, Vancouver, ISO, and other styles

31

Mohammed, Iman Subhi, and Israa Mohammed Alhamdani. "A fuzzy system for detection and classification of textile defects to ensure the quality of fabric production." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4277. http://dx.doi.org/10.11591/ijece.v9i5.pp4277-4286.

Full text

Abstract:

<p>The aim of this research focuses on construct a computerized system for textile defects detection. The system merges between image processing methods, statistical methods in addition to the Intelligent techniques via Neural Network and Fuzzy Logic. Gabor filters were used to identify edges and to highlight defective areas in fabric images, then to train the neural network on statistical and geometry features derived from fabric images to form the special neural network distinguish and classify defects into the fourteen categories, which are the most common defects in the textile facto

APA, Harvard, Vancouver, ISO, and other styles

32

Iman, Subhi Mohammed, and Mohammed Alhamdani Israa. "A fuzzy system for detection and classification of textile defects to ensure the quality of fabric production." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 4277–86. https://doi.org/10.11591/ijece.v9i5.pp4277-4286.

Full text

Abstract:

The aim of this research focuses on construct a computerized system for textile defects detection. The system merges between image processing methods, statistical methods in addition to the Intelligent techniques via Neural Network and Fuzzy Logic. Gabor filters were used to identify edges and to highlight defective areas in fabric images, then to train the neural network on statistical and geometry features derived from fabric images to form the special neural network distinguish and classify defects into the fourteen categories, which are the most common defects in the textile factory. The p

APA, Harvard, Vancouver, ISO, and other styles

33

Ban, Geunwoo, and Joonhyuk Yoo. "RT-SPeeDet: Real-Time IP–CNN-Based Small Pit Defect Detection for Automatic Film Manufacturing Inspection." Applied Sciences 11, no. 20 (2021): 9632. http://dx.doi.org/10.3390/app11209632.

Full text

Abstract:

Pits are defects that occur during the film manufacturing process; they appear in the micrometer scale, which makes distinguishing them with the human eye difficult. Existing defect detectors have poor recognition rates for small objects or require a considerable amount of time. To resolve these problems, we propose a real-time small pit defect detector (RT-SPeeDet), a two-stage detection model based on an image processing and convolutional neural network (IP–CNN) approach. The proposed method predicts boundary boxes using a lightweight image-processing algorithm optimized for pit defects, and

APA, Harvard, Vancouver, ISO, and other styles

34

Xinhai Li. "Defect Detection of Fabric Printing with Differential Attention." Journal of Information Systems Engineering and Management 10, no. 3 (2025): 1022–33. https://doi.org/10.52783/jisem.v10i3.6556.

Full text

Abstract:

Fabric defect detection is a major quality control process in the textile industry. However, compared with common detection tasks, printed fabric defect detection has several difficulties as follows: printed fabric pattern texture is complex; defects are of numerous types and vary greatly in size and shape; most of the defects are extremely small.Aiming at these problems and difficulties in real scenes, this paper firstly adopts the idea of template matching in change detection to eliminate the background pattern texture features of target images by using template image features.Aiming at the

APA, Harvard, Vancouver, ISO, and other styles

35

Dongyan, Cui, Xia Kewen, Hou Jingzhong, and Ali Ahmad. "Image De-noising on Strip Steel Surface Defect Using Improved Compressive Sensing Algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 540 ∼ 548. https://doi.org/10.12928/telkomnika.v15.i1.3164.

Full text

Abstract:

De-noising for the strip steel surface defect image is conductive to the accurate detection of the strip steel surface defects. In order to filter the Gaussian noise and salt and pepper noise of strip steel surface defect images, an improved compressive sensing algorithm was applied to defect image de-noising in this paper. First, the improved Regularized Orthogonal Matching Pursuit algorithm was described. Then, three typical surface defects (scratch, scar, surface upwarping) images were selected as the experimental samples. Last, detailed experimental tests were carried out to the strip stee

APA, Harvard, Vancouver, ISO, and other styles

36

Kim, Kwang-Baek, and Young Woon Woo. "Defect Detection in Ceramic Images Using Sigma Edge Information and Contour Tracking Method." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 160. http://dx.doi.org/10.11591/ijece.v6i1.9343.

Full text

Abstract:

In this paper, we suggest a method of detecting defects by applying Hough transform and least squares on ceramic images obtained from non-destructive testing. In the ceramic images obtained from non-destructive testing, the background area, where the defect does not exist, commonly shows gradual change of luminosity in vertical direction. In order to extract the background area which is going to be used in the detection of defects, Hough transform is performed to rotate the ceramic image in a way that the direction of overall luminosity change lies in the vertical direction as much as possible

APA, Harvard, Vancouver, ISO, and other styles

37

Kim, Kwang-Baek, and Young Woon Woo. "Defect Detection in Ceramic Images Using Sigma Edge Information and Contour Tracking Method." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 160. http://dx.doi.org/10.11591/ijece.v6i1.pp160-166.

Full text

Abstract:

In this paper, we suggest a method of detecting defects by applying Hough transform and least squares on ceramic images obtained from non-destructive testing. In the ceramic images obtained from non-destructive testing, the background area, where the defect does not exist, commonly shows gradual change of luminosity in vertical direction. In order to extract the background area which is going to be used in the detection of defects, Hough transform is performed to rotate the ceramic image in a way that the direction of overall luminosity change lies in the vertical direction as much as possible

APA, Harvard, Vancouver, ISO, and other styles

38

Shahsavarani, Sara, Fernando Lopez, Clemente Ibarra-Castanedo, and Xavier P. V. Maldague. "Advanced Image Stitching Method for Dual-Sensor Inspection." Sensors 24, no. 12 (2024): 3778. http://dx.doi.org/10.3390/s24123778.

Full text

Abstract:

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Conseq

APA, Harvard, Vancouver, ISO, and other styles

39

Masita, Katleho, Ali Hasan, and Thokozani Shongwe. "Defects Detection on 110 MW AC Wind Farm’s Turbine Generator Blades Using Drone-Based Laser and RGB Images with Res-CNN3 Detector." Applied Sciences 13, no. 24 (2023): 13046. http://dx.doi.org/10.3390/app132413046.

Full text

Abstract:

An effective way to perform maintenance on the wind turbine generator (WTG) blades installed in grid-connected wind farms is to inspect them using Unmanned Aerial Vehicles (UAV). The ability to detect wind turbine blade defects from these laser and RGB images captured by drones has been the subject of numerous studies. The issue that most applied techniques battle with is being able to locate different wind turbine blade defects with high confidence scores and precision. The accuracy of these models’ defect detection decreases due to varying testing image scales. This article proposes the Res-

APA, Harvard, Vancouver, ISO, and other styles

40

Cui, Peng, Nurjihan Ala Bidzikrillah, Jiancong Xu, and Yazhou Qin. "Application of the Semi-Supervised Learning Approach for Pavement Defect Detection." Sensors 24, no. 18 (2024): 6130. http://dx.doi.org/10.3390/s24186130.

Full text

Abstract:

Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to mo

APA, Harvard, Vancouver, ISO, and other styles

41

Acharya, Pradip, Tsuchin P. Chu, Khaled R. Ahmed, and Subash Kharel. "A Deep Learning Approach for Defect Detection and Segmentation in X-Ray Computed Tomography Slices of Additively Manufactured Components." International Journal of Artificial Intelligence & Applications 13, no. 4 (2022): 1–15. http://dx.doi.org/10.5121/ijaia.2022.13401.

Full text

Abstract:

Additive manufacturing is an emerging and crucial technology that can overcome the limitations of traditional manufacturing techniques to accurately manufacture highly complex parts. X-ray Computed Tomography (XCT) is a widely used method for non-destructive testing of AM parts. However, detection and segmentation of defects in XCT images of AM have many challenges due to contrast, size, and appearance of defects. This study developed deep learning techniques for detecting and segmenting defects in XCT images of AM. Due to a large number of required defect annotations, this paper applied image

APA, Harvard, Vancouver, ISO, and other styles

42

Zhang, Junsheng, Zhijie Guo, Tengyun Jiao, and Mingquan Wang. "Defect Detection of Aluminum Alloy Wheels in Radiography Images Using Adaptive Threshold and Morphological Reconstruction." Applied Sciences 8, no. 12 (2018): 2365. http://dx.doi.org/10.3390/app8122365.

Full text

Abstract:

In low-pressure casting, aluminum alloy wheels are prone to internal defects such as gas holes and shrinkage cavities, which call for X-ray inspection to ensure quality. Automatic defect segmentation of X-ray images is an important task in X-ray inspection of wheels. For this, a solution is proposed here that combines adaptive threshold segmentation algorithm and mathematical morphology reconstruction. First, the X-ray image of the wheel is smoothed, and then the smoothed image is subtracted from the original image, and the resulting difference image is binarized; the binary image resulting fr

APA, Harvard, Vancouver, ISO, and other styles

43

Abou-Taleb, Hemdan A., and Aya Tallah M. Sallam. "ON-LINE FABRIC DEFECT DETECTION AND FULL CONTROL IN A CIRCULAR KNITTING MACHINE." AUTEX Research Journal 8, no. 1 (2008): 21–29. http://dx.doi.org/10.1515/aut-2008-080105.

Full text

Abstract:

Abstract This study has shown that image analysis has great potential to provide reliable measurements for detecting defects in knitted fabrics. Using the principles of image analysis, an automatic fabric evaluation system, which enables automatic computerised defect detection -(analysis of knitted fabrics) was developed. On-line fabric defect detection was tested automatically by analysing fabric images captured by a digital camera. The results of the automatic fabric defect detection correspond well with the experimental values. Therefore, it is shown that the developed image capturing and a

APA, Harvard, Vancouver, ISO, and other styles

44

Hu, Feng, Jie Gong, Han Fu, and Wenliang Liu. "Fabric Defect Detection Method Using SA-Pix2pix Network and Transfer Learning." Applied Sciences 14, no. 1 (2023): 41. http://dx.doi.org/10.3390/app14010041.

Full text

Abstract:

This paper proposes a fabric defect detection algorithm based on the SA-Pix2pix network and transfer learning to address the issue of insufficient accuracy in detecting complex pattern fabric defects in scenarios with limited sample data. Its primary contribution lies in treating defects as disruptions to the fabric’s texture. It leverages a generative adversarial network to reconstruct defective images, restoring them to images of normal fabric texture. Subsequently, the reconstituted images are subjected to dissimilarity calculations against defective images, leading to image segmentation fo

APA, Harvard, Vancouver, ISO, and other styles

45

Wang, Xuan, Wei Liu, Hui Cao, and Dong Ping Ma. "Analysis and Study of the Steel Plate Surface Defects Image Processing and the Fractal Dimension Characteristics." Advanced Materials Research 482-484 (February 2012): 1773–76. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.1773.

Full text

Abstract:

Steel surface defect detection is the key point of this research. The paper mainly focuses on the image processing and image feature extraction of the steel plate surface. The paper also focuses on the calculating procedure and results of the fractal dimension in different defects images. It can be concluded from the results of the study, fractal dimension of the defect images becomes an important feature of the steel plate surface image pattern recognition.

APA, Harvard, Vancouver, ISO, and other styles

46

Liu, Yan Bei, Zhi Tao Xiao, Fang Zhang, and Jun Wu. "Fabric Defect Detection Method Based on Gabor Filters." Advanced Materials Research 301-303 (July 2011): 229–34. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.229.

Full text

Abstract:

In this paper, a fabric defect detection method based on Gabor filter bank is present. In this method, the parameters of Gabor filter bank depend on the fabric texture feature. Using the Gabor filter bank with multi-frequency and multi-orientation, a textile image produces multi-image. Then, the images are reconstructed into one image for detecting defects. It is illustrated that most kinds of defects are correctly detected and segmented. The experimental results show that the algorithm is robust and has good detection effect.

APA, Harvard, Vancouver, ISO, and other styles

47

Wang, Yong Qing, Peng Chao Zhang, Shi Yuan Wang, et al. "The Application of Infrared Image Technology in the Detection of Local Defects of Solar Cell Module." Advanced Materials Research 347-353 (October 2011): 1206–9. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.1206.

Full text

Abstract:

If there are local defects in the solar cell module, the output efficiency of the module will decrease. This article introduces the detection of local defect of solar cell module based on the infrared image technology. It finally establishes an image library which contains some infrared images of seven kinds of defects and a basic standard which can judge the quality of the module. The reasons of some defects are listed in the article. The unqualified rate will greatly decrease for the technique. The research is helpful to eliminate the module that contains recessive defects. The life of the m

APA, Harvard, Vancouver, ISO, and other styles

48

Abdillah, Muhammad Hasya, Muhammad Arip Putra Sabilah, Andika Suherman, and Dwi Astharini. "Product Defect Detection using Image Template Matching with MATLAB." EXSACT-A 1 (December 31, 2023): 63. http://dx.doi.org/10.36722/exc.v1i1.2344.

Full text

Abstract:

Abstract— In industrial manufacturing processes, ensuring the quality of products is crucial. This paper proposes a system for detecting defects in products using image template matching techniques implemented in MATLAB. The system's primary function is to compare captured images of products with predefined templates to identify potential defects accurately. The method employed in this system is template matching, a well-established approach in image analysis that allows for efficient defect detection. MATLAB, a widely used software tool for image processing, provides the necessary functionali

APA, Harvard, Vancouver, ISO, and other styles

49

Fithry Senin, Syahrul, Khairullah Yusuf, Amer Yusuf, and Rohamezan Rohim. "Machine Learning Application for Concrete Surface Defects Automatic Damage Classification." Jurnal Kejuruteraan 36, no. 1 (2024): 21–27. http://dx.doi.org/10.17576/jkukm-2024-36(1)-03.

Full text

Abstract:

Defects damage in concrete structures are an important measure of structural integrity and serviceability. In the context of investigating the condition of concrete surface that has defects, a visual inspection is usually performed. However, this method is subjective, tedious, time-consuming, and complicated, requiring access to many components of a large project design. Therefore, a Machine Learning classifier for concrete surface defect classification using the Discriminant Analysis Classifier was introduced to more accurately extract the types of concrete surface defects information from th

APA, Harvard, Vancouver, ISO, and other styles

50

Zhou, Qinbang, Renwen Chen, Bin Huang, Chuan Liu, Jie Yu, and Xiaoqing Yu. "An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods." Sensors 19, no. 3 (2019): 644. http://dx.doi.org/10.3390/s19030644.

Full text

Abstract:

Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers’ first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!