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1
Peng, Peiran, Ying Wang, Can Hao, Zhizhong Zhu, Tong Liu, and Weihu Zhou. "Automatic Fabric Defect Detection Method Using PRAN-Net." Applied Sciences 10, no. 23 (November 26, 2020): 8434. http://dx.doi.org/10.3390/app10238434.
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Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.
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Zhao, Weidong, Hancheng Huang, Dan Li, Feng Chen, and Wei Cheng. "Pointer Defect Detection Based on Transfer Learning and Improved Cascade-RCNN." Sensors 20, no. 17 (September 1, 2020): 4939. http://dx.doi.org/10.3390/s20174939.
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To meet the practical needs of detecting various defects on the pointer surface and solve the difficulty of detecting some defects on the pointer surface, this paper proposes a transfer learning and improved Cascade-RCNN deep neural network (TICNET) algorithm for detecting pointer defects. Firstly, the convolutional layers of ResNet-50 are reconstructed by deformable convolution, which enhances the learning of pointer surface defects by feature extraction network. Furthermore, the problems of missing detection caused by internal differences and weak features are effectively solved. Secondly, the idea of online hard example mining (OHEM) is used to improve the Cascade-RCNN detection network, which achieve accurate classification of defects. Finally, based on the fact that common pointer defect dataset and pointer defect dataset established in this paper have the same low-level visual characteristics. The network is pre-trained on the common defect dataset, and weights are transferred to the defect dataset established in this paper, which reduces the training difficulty caused by too few data. The experimental results show that the proposed method achieves a 0.933 detection rate and a 0.873 mean average precision when the threshold of intersection over union is 0.5, and it realizes high precision detection of pointer surface defects.
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Zz, Mi, C. Cong, Y. Cheng, and Zhang Hm. "Study on defects detection technique of precise optical element." E3S Web of Conferences 53 (2018): 01037. http://dx.doi.org/10.1051/e3sconf/20185301037.
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Aiming at the problems of low efficiency of traditional detection methods for surface defects of precision optical element and inconvenient detection for optical elements of different calibers, a adjustable optical element defects detecting device for large laser devices is designed. The key technical points of system composition, detection environment, illumination design and image stitching are expounded. According to the characteristics of surface defects of optical element, such as the difference of contour, gray scale, contrast and ambiguity, a classification method based on FCM is proposed. The experimental results show that the system can realize the automatic detection of surface defects, also it can effectively distinguishes micron-scale defects and has good defect recognition performance. The overall average recognition rate reached to 93.3%.
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Jian, Chuan Xia, Jian Gao, and Xin Chen. "A Review of TFT-LCD Panel Defect Detection Methods." Advanced Materials Research 734-737 (August 2013): 2898–902. http://dx.doi.org/10.4028/www.scientific.net/amr.734-737.2898.
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TFT-LCD panel defect detection has been one of the difficulties in this field because of fuzzy defect boundary, low contrast between defects and background, and low detection speed. The structure of TFT-LCD panels and classification are introduced. Through the analysis of panel defect features, current detection methods for the TFT-LCD panel defects are reviewed. The key technologies of feature extraction and defect classification are analyzed in the defect image recognition of TFT-LCD panel. Meanwhile the methods of fuzzy boundary defect segmentation, image subtraction and image filtering are also discussed. Finally, the characteristics and advantages of these detection methods are concluded, and several key issues for the TFT-LCD defect detection have been proposed for future development.
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Wu, Bao Hua, Lei Duan, Gui Hua Wang, Hai Yang Wang, and Jing Peng. "Gene Expression Programming Based Classification for Automated Birth Defects Detection." Applied Mechanics and Materials 197 (September 2012): 508–14. http://dx.doi.org/10.4028/www.scientific.net/amm.197.508.
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With the rapid development of digital medicine, improving the diagnostic accuracy for birth defects (BD) by using data mining techniques has been paid more attentions by researchers. In this paper, an automated classification technique based on Gene Expression Programming (GEP) to detect the defect infants, named Birth Defects Detection based on Gene Expression Programming (BDD-GEP) is proposed. The main contributions of this paper include: (1) proposing two contrast inequalities (CIs) for birth defects detection: the defection contrasts to normal and the normal contrasts to defection, (2) designing a new fitness function to mine the normal and defect CIs by GEP, (3) presenting a method to select useful CIs for classification, (4) implementing the BDD-GEP algorithm through combining the proposed CIs with k-Nearest Neighbor algorithm. In order to evaluate the proposed classification method, 11,897 infant samples from national center for birth defects monitoring of China were used, and the method was compared with several existing classification methods. The experimental results show that the overall detection accuracy of BDD-GEP was as high as 87.8%. Specifically, the F-measure of the detect samples was about 70.2%, and the F-measure of the normal samples was about 92.3%.
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Czimmermann, Tamás, Gastone Ciuti, Mario Milazzo, Marcello Chiurazzi, Stefano Roccella, Calogero Maria Oddo, and Paolo Dario. "Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY." Sensors 20, no. 5 (March 6, 2020): 1459. http://dx.doi.org/10.3390/s20051459.
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This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.
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Lu, Manhuai, and Chin-Ling Chen. "Detection and Classification of Bearing Surface Defects Based on Machine Vision." Applied Sciences 11, no. 4 (February 18, 2021): 1825. http://dx.doi.org/10.3390/app11041825.
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Surface defects on bearings can directly affect the service life and reduce the performance of equipment. At present, the detection of bearing surface defects is mostly done manually, which is labor-intensive and results in poor stability. To improve the inspection speed and the defect recognition rate, we proposed a bearing surface defect detection and classification method using machine vision technology. The method makes two main contributions. It proposes a local multi-neural network (Lc-MNN) image segmentation algorithm with the wavelet transform as the classification feature. The precision segmentation of the defect image is accomplished in three steps: wavelet feature extraction, Lc-MNN region division, and Lc-MNN classification. It also proposes a feature selection algorithm (SCV) that makes comprehensive use of scalar feature selection, correlation analysis, and vector feature selection to first remove similar features through correlation analysis, further screen the results with a scalar feature selection algorithm, and finally select the classification features using a feature vector selection algorithm. Using 600 test samples with three types of defect in the experiment, an identification rate of 99.5% was achieved without the need for large-scale calculation. The comparison tests indicated that the proposed method can achieve efficient feature selection and defect classification.
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Jiang, Qingsheng, Dapeng Tan, Yanbiao Li, Shiming Ji, Chaopeng Cai, and Qiming Zheng. "Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning." Applied Sciences 10, no. 1 (December 20, 2019): 87. http://dx.doi.org/10.3390/app10010087.
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Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection.
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Liu, Zixi, Zhengliang Hu, Longxiang Wang, Tianshi Zhou, Jintao Chen, Zhenyu Zhu, Hao Sui, Hongna Zhu, and Guangming Li. "Effective detection of metal surface defects based on double-line laser ultrasonic with convolutional neural networks." Modern Physics Letters B 35, no. 15 (April 15, 2021): 2150263. http://dx.doi.org/10.1142/s0217984921502638.
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The time–frequency analysis by smooth Pseudo-Wigner-Ville distribution (SPWVD) is utilized for the double-line laser ultrasonic signal processing, and the effective detection of the metal surface defect is achieved. The double-line source laser is adopted for achieving more defects information. The simulation model by using finite element method is established in a steel plate with three typical metal surface defects (i.e. crack, air hole and surface scratch) in detail. Besides, in order to improve the time resolution and frequency resolution of the signal, the SPWVD method is mainly used. In addition, the deep learning defect classification model based on VGG convolutional neural network (CNN) is set up, also, the data enhancement method is adopted to extend training data and improve the defects detection properties. The results show that, for different types of metal surface defects with sub-millimeter size, the classification accuracy of crack, air holes and scratch surface are 94.6%, 94% and 94.6%, respectively. The SPWVD and CNN algorithm for processing the laser ultrasonic signal and defects classification supplies a useful way to get the defect information, which is helpful for the ultrasonic signal processing and material evaluation.
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Lu, Yuzhen, and Renfu Lu. "siritool: A Matlab Graphical User Interface for Image Analysis in Structured-Illumination Reflectance Imaging for Fruit Defect Detection." Transactions of the ASABE 63, no. 4 (2020): 1037–47. http://dx.doi.org/10.13031/trans.13612.
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HIGHLIGHTSA Matlab GUI, siriTool, was developed for structured-illumination reflectance imaging.siriTool enables image preprocessing, feature extraction, and classification.siriTool was demonstrated for detection of spot defects on pickling cucumbers.Abstract. Structured-illumination reflectance imaging (SIRI) is an emerging imaging modality that provides more useful discriminative features for enhancing detection of defects in fruit and other horticultural and food products. In this study, we developed a Matlab graphical user interface (GUI), siriTool (available at https://codeocean.com/capsule/5699671/tree), to facilitate image analysis in SIRI for fruit defect detection. The GUI enables image preprocessing (i.e., demodulation, object segmentation, and image enhancement), feature extraction and selection, and classification. Demodulation is done using a three-phase or two-phase approach depending on the image data acquired, object segmentation (or background removal) is implemented based on automatic unimodal thresholding, and image enhancement is achieved using fast bi-dimensional empirical decomposition followed by selective image reconstructions. For defect detection, features of different types are extracted from the enhanced images, and feature selection is performed to reduce the feature set. Finally, the full or reduced set of features are then input into different classifiers, e.g., support vector machine (SVM), for image-level classifications. An application example is presented on the detection of yellowish subsurface spot defects in pickling cucumbers. SIRI achieved over 98% classification accuracies based on SVM modeling with the extracted features, which were significantly better than the accuracies obtained under uniform illumination. Keywords: Defect detection, Demodulation, Image enhancement, Machine learning, Matlab, Structured illumination.
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He, Yuan, Han-Dong Zhang, Xin-Yue Huang, and Francis Eng Hock Tay. "Fabric Defect Detection based on Improved Faster RCNN." International Journal of Artificial Intelligence & Applications 12, no. 04 (July 31, 2021): 23–32. http://dx.doi.org/10.5121/ijaia.2021.12402.
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In the production process of fabric, defect detection plays an important role in the control of product quality. Consider that traditional manual fabric defect detection method are time-consuming and inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can infer the attention map from the intermediate feature map and multiply the attention map to adaptively refine the feature. This method improve the performance of classification and detection without increase the computation-consuming. The experiment results show that Faster RCNN with attention module can efficient improve the classification accuracy.
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Benzahioul, Samia, Abderrezak Metatla, Adlen Kerboua, Dimitri Lefebvre, and Riad Bendib. "Use of Support Vector Machines for Classification of Defects in the Induction Motor." Acta Universitatis Sapientiae, Electrical and Mechanical Engineering 11, no. 1 (December 1, 2019): 1–21. http://dx.doi.org/10.2478/auseme-2019-0001.
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Abstract The classification and detection of defects play an important role in different disciplines. Research is oriented towards the development of approaches for the early detection and classification of defects in electrical drive systems. This paper, proposes a new approach for the classification of induction motor defects based on image processing and pattern recognition. The proposed defect classification approach was carried out in four distinct stages. In the first step, the stator currents were represented in the 3D space and projected onto the 2D space. In the second step, the projections obtained were transformed into images. In the third step, extraction of features whereas the Histogram of Oriented Gradient (HOG) is used to construct a descriptor based on several sizes of cells. In the fourth step, a method of classifying the induction motor defects based on the Support Vector Machine (SVM) was applied. The evaluation results of the developed approach show the efficiency and the precision of classification of the proposed approach.
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Boikov, Aleksei, Vladimir Payor, Roman Savelev, and Alexandr Kolesnikov. "Synthetic Data Generation for Steel Defect Detection and Classification Using Deep Learning." Symmetry 13, no. 7 (June 29, 2021): 1176. http://dx.doi.org/10.3390/sym13071176.
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The paper presents a methodology for training neural networks for vision tasks on synthesized data on the example of steel defect recognition in automated production control systems. The article describes the process of dataset procedural generation of steel slab defects with a symmetrical distribution. The results of training two neural networks Unet and Xception on a generated data grid and testing them on real data are presented. The performance of these neural networks was assessed using real data from the Severstal: Steel Defect Detection set. In both cases, the neural networks showed good results in the classification and segmentation of surface defects of steel workpieces in the image. Dice score on synthetic data reaches 0.62, and accuracy—0.81.
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Wang, Shuai, Xiaojun Xia, Lanqing Ye, and Binbin Yang. "Automatic Detection and Classification of Steel Surface Defect Using Deep Convolutional Neural Networks." Metals 11, no. 3 (February 26, 2021): 388. http://dx.doi.org/10.3390/met11030388.
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Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models.
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Kaur, Beant, G. Kaur, and A. Kaur. "Detection and Classification of Printed Circuit Boards Defects." Open Transactions on Information Processing 2014, no. 1 (March 31, 2014): 8–16. http://dx.doi.org/10.15764/otip.2014.01002.
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Qi, Ai Ling, Jing Fang Wang, Frank Wang, Unekwu Idachaba, and Gbola Akanmu. "Welding Defect Classification of Ultrasonic Detection Based on PCA and KNN." Applied Mechanics and Materials 380-384 (August 2013): 902–6. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.902.
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Aiming to problems of welding defect classification in the ultrasonic detection, according to the characteristics of welding defect, a classification method based on PCA and KNN is proposed in order to solve the problem of ultrasonic testing signal feature extraction and defect recognition. For the impact of the redundant attributes of feature extraction on the classification accuracy, in this paper, an ultrasonic flaw feature extraction algorithm based on PCA is proposed. Ultrasonic flaw intelligent classification is always a difficult problem in NDT. KNN algorithm is proposed to classify the different defects. Compared with BP algorithm, experiment results show that the model based on PCA and KNN can get stable classification results, high accuracy, and can effectively improve the classification efficiency.
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Zhou, Fei, Guihua Liu, Feng Xu, and Hao Deng. "A Generic Automated Surface Defect Detection Based on a Bilinear Model." Applied Sciences 9, no. 15 (August 3, 2019): 3159. http://dx.doi.org/10.3390/app9153159.
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Aiming at the problems of complex texture, variable interference factors and large sample acquisition in surface defect detection, a generic method of automated surface defect detection based on a bilinear model was proposed. To realize the automatic classification and localization of surface defects, a new Double-Visual Geometry Group16 (D-VGG16) is firstly designed as feature functions of the bilinear model. The global and local features fully extracted from the bilinear model by D-VGG16 are output to the soft-max function to realize the automatic classification of surface defects. Then the heat map of the original image is obtained by applying Gradient-weighted Class Activation Mapping (Grad-CAM) to the output features of D-VGG16. Finally, the defects in the original input image can be located automatically after processing the heat map with a threshold segmentation method. The training process of the proposed method is characterized by a small sample, end-to-end, and is weakly-supervised. Furthermore, experiments are performed on two public and two industrial datasets, which have different defective features in texture, shape and color. The results show that the proposed method can simultaneously realize the classification and localization of defects with different defective features. The average precision of the proposed method is above 99% on the four datasets, and is higher than the known latest algorithms.
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Moganam, Praveen Kumar, and Denis Ashok Sathia Seelan. "Perceptron Neural Network Based Machine Learning Approaches for Leather Defect Detection and Classification." Instrumentation Mesure Métrologie 16, no. 6 (December 29, 2020): 421–29. http://dx.doi.org/10.18280/i2m.190603.
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Detection of defects in a typical leather surface is a difficult task due to the complex, non-homogenous and random nature of texture pattern. This paper presents a texture analysis based leather defect identification approach using a neural network classification of defective and non-defective leather. In this work, Gray Level Co-occurrence Matrix (GLCM) is used for extracting different statistical texture features of defective and non-defective leather. Based on the labelled data set of texture features, perceptron neural network classifier is trained for defect identification. Five commonly occurring leather defects such as folding marks, grain off, growth marks, loose grain and pin holes were detected and the classification results of perceptron network are presented. Proposed method was tested for the image library of 1232 leather samples and the accuracy of classification for the defect detection using confusion matrix is found to be 94.2%. Proposed method can be implemented in the industrial environment for the automation of leather inspection process.
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Deng, Weiquan, Jun Bao, and Bo Ye. "Defect Image Recognition and Classification for Eddy Current Testing of Titanium Plate Based on Convolutional Neural Network." Complexity 2020 (October 10, 2020): 1–10. http://dx.doi.org/10.1155/2020/8868190.
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In the actual production environment, the eddy current imaging inspection of titanium plate defects is prone to scan shift, scale distortion, and noise interference in varying degrees, which leads to the defect false detection and even missed inspection. In view of this problem, a novel image recognition and classification method based on convolutional neural network (CNN) for eddy current detection of titanium plate defects is proposed. By constructing a variety of experimental conditions and collecting defect signals, the characteristics of eddy current testing (ECT) signals for titanium plate defects are analyzed, and then the convolution structure and learning parameters are set. The structural characteristics of local connectivity and shared weights of CNN have better feature learning and characterization capabilities for titanium plate defect images under scan shift, scale distortion, and strong noise interference. The results prove that, compared with other deep learning and classical machine learning methods, the CNN has a higher recognition and classification accuracy for the defect eddy current image of the titanium plate in the complex detection environment.
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Zhou, Haiyan, Zilong Zhuang, Ying Liu, Yang Liu, and Xiao Zhang. "Defect Classification of Green Plums Based on Deep Learning." Sensors 20, no. 23 (December 7, 2020): 6993. http://dx.doi.org/10.3390/s20236993.
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The green plum is rich in amino acids, lipids, inorganic salts, vitamins, and trace elements. It has high nutritional value and medicinal value and is very popular among Chinese people. However, green plums are susceptible to collisions and pests during growth, picking, storage, and transportation, causing surface defects, affecting the quality of green plums and their products and reducing their economic value. In China, defect detection and grading of green plum products are still performed manually. Traditional manual classification has low accuracy and high cost, which is far from meeting the production needs of green plum products. In order to improve the economic value of green plums and their products and improve the automation and intelligence level of the product production process, this study adopted deep learning methods based on a convolutional neural network and cost-effective computer vision technology to achieve efficient classification of green plum defects. First, a camera and LEDs were used to collect 1240 green plum images of RGB, and the green plum experimental classification standard was formulated and divided into five categories, namely, rot, spot, scar, crack, and normal. Images were randomly divided into a training set and test set, and the number of images of the training set was expanded. Then, the stochastic weight averaging (SWA) optimizer and w-softmax loss function were used to improve the VGG network, which was trained and tested to generate a green plum defect detection network model. The average recognition accuracy of green plum defects was 93.8%, the test time for each picture was 84.69 ms, the recognition rate of decay defect was 99.25%, and the recognition rate of normal green plum was 95.65%. The results were compared with the source VGG network, resnet18 network, and green lemon network. The results show that for the classification of green plum defects, the recognition accuracy of the green plum defect detection network increased by 9.8% and 16.6%, and the test speed is increased by 1.87 and 6.21 ms, respectively, which has certain advantages.
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Kusumawardani, Rindi, and Putu Dana Karningsih. "Detection and Classification of Canned Packaging Defects Using Convolutional Neural Network." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 4, no. 1 (March 10, 2021): 1–11. http://dx.doi.org/10.21070/prozima.v4i1.1280.
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Packaging is one of the important aspects of a product’s identity. The good and adorable packaging can increase product competitiveness because it gives a perception to the customers of good quality products. Therefore, a good packaging display is necessary so that packaging quality inspection is very important. Automated defect detection can help to reduce human error in the inspection process. Convolutional Neural Network (CNN) is an approach that can be used to detect and classify a packaging condition. This paper presents an experiment that compares 5 network models, i.e. ShuffleNet, GoogLeNet, ResNet18, ResNet50, and Resnet101, each network given the same parameters. The dataset is an image of cans packaging which is divided into 3 classifications, No Defect, Minor Defect, and Major Defect. The experimental result shows that network architecture models of ResNet50 and ResNet101 provided the best result for cans defect classification than the other network models, with 95,56% for testing accuracy. The five models have the testing accuracy above 90%, so it can be concluded that all network models are ideal for detecting the packaging defect and defect classification for the cans product.
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Jeyaraj, Pandia Rajan, and Edward Rajan Samuel Nadar. "Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm." International Journal of Clothing Science and Technology 31, no. 4 (August 5, 2019): 510–21. http://dx.doi.org/10.1108/ijcst-11-2018-0135.
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Purpose The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm. Design/methodology/approach To make a fast and effective classification of fabric defect, the authors have considered a characteristic of texture, namely its colour. A deep convolutional neural network is formed to learn from the training phase of various defect data sets. In the testing phase, the authors have utilised a learning feature for defect classification. Findings The improvement in the defect classification accuracy has been achieved by employing deep learning algorithm. The authors have tested the defect classification accuracy on six different fabric materials and have obtained an average accuracy of 96.55 per cent with 96.4 per cent sensitivity and 0.94 success rate. Practical implications The authors had evaluated the method by using 20 different data sets collected from different raw fabrics. Also, the authors have tested the algorithm in standard data set provided by Ministry of Textile. In the testing task, the authors have obtained an average accuracy of 94.85 per cent, with six defects being successfully recognised by the proposed algorithm. Originality/value The quantitative value of performance index shows the effectiveness of developed classification algorithm. Moreover, the computational time for different fabric processing was presented to verify the computational range of proposed algorithm with the conventional fabric processing techniques. Hence, this proposed computer vision-based fabric defects detection system is used for an accurate defect detection and computer-aided analysis system.
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Srividhya, R., K. Shanmugapriya, and K. Sindhu Priya. "Automatic Detection of Surface Defects in Industrial Materials Based on Image Processing." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 61. http://dx.doi.org/10.14419/ijet.v7i3.34.18717.
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In the field of industry, corrosion and defects are amongst the most frequent operations. Industrial Materials have periodic defects that are difficult to detect during production even by experienced human inspectors. Defects are difficult to detect during production even by experienced human inspectors. Usually, the colour transfer process contains an image segmentation phase and an image construction phase. Therefore, we introduce an image processing method for automatically detecting the defects in surfaces. We show how barely visible defect can be optically enhanced to improve annual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated detection. Image enhancement is performed by applying manual calculation. We implement this simulation using MATLAB R2013a. Results show that the proposed allows training both tested classifiers with good classification rates around 98.9%.
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Le, Ngoc Tuyen, Jing-Wein Wang, Chou-Chen Wang, and Tu N. Nguyen. "Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification." Sensors 19, no. 24 (December 14, 2019): 5538. http://dx.doi.org/10.3390/s19245538.
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No matter your experience level or budget, there is a great ski goggle waiting to be found.Goggles are an essential part of skiing or snowboarding gear to protect your eyes from harsh environmental elements and injury. In the ski goggles manufacturing industry, defects, especially on the lens surface, are unavoidable. However, defect detection and classification by visual inspection in the manufacturing process is very difficult. To overcome this problem, a novel framework based on machine vision is presented, named as the ski goggles lens defect detection, with five high-resolution cameras and custom-made lighting field to achieve a high-quality ski goggles lens image. Next, the defects on the lens of ski goggles are detected by using parallel projection in opposite directions based on adaptive energy analysis. Before being put into the classification system, the defect images are enhanced by an adaptive method based on the high-order singular value decomposition (HOSVD). Finally, dust and five types of defect images are classified into six types, i.e., dust, spotlight (type 1, type 2, type 3), string, and watermark, by using the developed classification algorithm. The defect detection and classification results of the ski goggles lens are compared to the standard quality of the manufacturer. Experiments using 120 ski goggles lens samples collected from the largest manufacturer in Taiwan are conducted to validate the performance of the proposed framework. The accurate defect detection rate is 100% and the classification accuracy rate is 99.3%, while the total running time is short. The results demonstrate that the proposed method is sound and useful for ski goggles lens inspection in industries.
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Sheu, Ruey-Kai, Ya-Hsin Teng, Chien-Hao Tseng, and Lun-Chi Chen. "Apparatus and Method of Defect Detection for Resin Films." Applied Sciences 10, no. 4 (February 11, 2020): 1206. http://dx.doi.org/10.3390/app10041206.
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A defect inspection of resin films involves processes of detecting defects, size measuring, type classification and reflective action planning. It is not only a process requiring heavy investment in workforce, but also a tension between quality assurance with a 50-micrometer tolerance and visibility of the naked eye. To solve the difficulties of the workforce and time consumption processes of defect inspection, an apparatus is designed to collect high-quality images in one shot by leveraging a large field-of-view microscope at 2K resolution. Based on the image dataset, a two-step method is used to first locate possible defects and predict their types by a defect-shape-based deep learning model using the LeNet-5-adjusted network. The experimental results show that the proposed method can precisely locate the position and accurately inspect the fine-grained defects of resin films.
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Gao, Mingyu, Fei Wang, Peng Song, Junyan Liu, and DaWei Qi. "BLNN: Multiscale Feature Fusion-Based Bilinear Fine-Grained Convolutional Neural Network for Image Classification of Wood Knot Defects." Journal of Sensors 2021 (August 17, 2021): 1–18. http://dx.doi.org/10.1155/2021/8109496.
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Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves the wood utilization. The traditional neural network technique is unemployed for the wood defect detection of optical image used, which results from a long training time, low recognition accuracy, and nonautomatic extraction of defect image features. In this paper, a wood knot defect detection model (so-called BLNN) combined deep learning is reported. Two subnetworks composed of convolutional neural networks are trained by Pytorch. By using the feature extraction capabilities of the two subnetworks and combining the bilinear join operation, the fine-grained features of the image are obtained. The experimental results show that the accuracy has reached up 99.20%, and the training time is obviously reduced with the speed of defect detection about 0.0795 s/image. It indicates that BLNN has the ability to improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.
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Zhu, Jinsong, and Jinbo Song. "An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges." Applied Sciences 10, no. 3 (February 2, 2020): 972. http://dx.doi.org/10.3390/app10030972.
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This paper mainly improves the visual geometry group network-16 (VGG-16), which is a classic convolutional neural network (CNN), to classify the surface defects on cement concrete bridges in an accurate manner. Specifically, the number of fully connected layers was reduced by one, and the Softmax classifier was replaced with a Softmax classification layer with seven defect tags. The weight parameters of convolutional and pooling layers were shared in the pre-trained model, and the rectified linear unit (ReLU) function was taken as the activation function. The original images were collected by a road inspection vehicle driving across bridges on national and provincial highways in Jiangxi Province, China. The images on surface defects of cement concrete bridges were selected, and divided into a training set and a test set, and preprocessed through morphology-based weight adaptive denoising. To verify its performance, the improved VGG-16 was compared with traditional shallow neural networks (NNs) like the backpropagation neural network (BPNN), support vector machine (SVM), and deep CNNs like AlexNet, GoogLeNet, and ResNet on the same sample dataset of surface defects on cement concrete bridges. Judging by mean detection accuracy and top-5 accuracy, our model outperformed all the contrastive methods, and accurately differentiated between images with seven classes of defects such as normal, cracks, fracturing, plate fracturing, corner rupturing, edge/corner exfoliation, skeleton exposure, and repairs. The results indicate that our model can effectively extract the multi-layer features from surface defect images, which highlights the edges and textures. The research findings shed important new light on the detection of surface defects and classification of defect images.
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Deng, Weiquan, Bo Ye, Jun Bao, Guoyong Huang, and Jiande Wu. "Classification and Quantitative Evaluation of Eddy Current Based on Kernel-PCA and ELM for Defects in Metal Component." Metals 9, no. 2 (February 1, 2019): 155. http://dx.doi.org/10.3390/met9020155.
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Eddy current testing technology is widely used in the defect detection of metal components and the integrity evaluation of critical components. However, at present, the evaluation and analysis of defect signals are still mostly based on artificial evaluation. Therefore, the evaluation of defects is often subjectively affected by human factors, which may lead to a lack in objectivity, accuracy, and reliability. In this paper, the feature extraction of non-linear signals is carried out. First, using the kernel-based principal component analysis (KPCA) algorithm. Secondly, based on the feature vectors of defects, the classification of an extreme learning machine (ELM) for different defects is studied. Compared with traditional classifiers, such as artificial neural network (ANN) and support vector machine (SVM), the accuracy and rapidity of ELM are more advantageous. Based on the accurate classification of defects, the linear least-squares fitting is used to further quantitatively evaluate the defects. Finally, the experimental results have verified the effectiveness of the proposed method, which involves automatic defect classification and quantitative analysis.
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Ehteram, Saeedreza. "DCT AND MLP IN THE APPLICATION OF MAGNETIC FLUX LEAKAGE DEFECT DETECTION." Acta Tecnología 6, no. 4 (December 31, 2020): 119–22. http://dx.doi.org/10.22306/atec.v6i4.96.
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Non-Destructive Testing (NDT) is known as a harmless technique for industrial pipeline cyclic inspection. This way tries to find out defected parts of a device used in industry with a test by means of non itself destroying. Many ways are known and employed in NDT procedure. MFL or magnetic Flux Leakage is one of well-known and so efficient ones is widely used to find out defects in metal surface. Emission of magnetic field into device surface and recording reflected emission lead to complete a database of defect and no defect for an especial task. Then mathematical equations could help to provide normalization and classification ahead. Defect and non-defect detection are an essential and cost loss technique for analyse data from cyclic inspections. For this purpose a combination of neural networks is designed and trained in the best performance and with optimum accuracy rate. In this model Classification is done via Multilayer Perceptrons (MLP). Two level of classification is applied. First defect categorization and then defect or non-defect detection. In this paper a mathematical function named DCT or Discrete Cosine Transform is applied in pure database for data compression. This function provides a view on database in real component of frequency domain. By composing DCT function with a neural network group, this algorithm provides 97.3 percent accuracy rate in defect detection of MFL signals.
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Xie Gesa, 解格飒, 王红军 Wang Hongjun, 王大森 Wang Dasen, 田爱玲 Tian Ailing, 刘丙才 Liu Bingcai, 朱学亮 Zhu Xueliang, and 刘卫国 Liu Weiguo. "Study on classification and detection of supersmooth surface defects." Infrared and Laser Engineering 48, no. 11 (2019): 1113003. http://dx.doi.org/10.3788/irla201948.1113003.
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Shady, Ebraheem, Yasser Gowayed, Mohamed Abouiiana, Safinaz Youssef, and Christopher Pastore. "Detection and Classification of Defects in Knitted Fabric Structures." Textile Research Journal 76, no. 4 (April 2006): 295–300. http://dx.doi.org/10.1177/0040517506053906.
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Branca, Antonella. "Automated system for detection and classification of leather defects." Optical Engineering 35, no. 12 (December 1, 1996): 3485. http://dx.doi.org/10.1117/1.601111.
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Stivanello, Mauricio Edgar, Saulo Vargas, Mario Lucio Roloff, and Marcelo Ricardo Stemmer. "Automatic Detection and Classification of Defects in Knitted Fabrics." IEEE Latin America Transactions 14, no. 7 (July 2016): 3065–73. http://dx.doi.org/10.1109/tla.2016.7587603.
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Wen, Hao, Chang Huang, and Shengmin Guo. "The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts." Materials 14, no. 10 (May 15, 2021): 2575. http://dx.doi.org/10.3390/ma14102575.
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Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.
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Xu, Pengcheng, Zhongyuan Guo, Lei Liang, and Xiaohang Xu. "MSF-Net: Multi-Scale Feature Learning Network for Classification of Surface Defects of Multifarious Sizes." Sensors 21, no. 15 (July 29, 2021): 5125. http://dx.doi.org/10.3390/s21155125.
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In the field of surface defect detection, the scale difference of product surface defects is often huge. The existing defect detection methods based on Convolutional Neural Networks (CNNs) are more inclined to express macro and abstract features, and the ability to express local and small defects is insufficient, resulting in an imbalance of feature expression capabilities. In this paper, a Multi-Scale Feature Learning Network (MSF-Net) based on Dual Module Feature (DMF) extractor is proposed. DMF extractor is mainly composed of optimized Concatenated Rectified Linear Units (CReLUs) and optimized Inception feature extraction modules, which increases the diversity of feature receptive fields while reducing the amount of calculation; the feature maps of the middle layer with different sizes of receptive fields are merged to increase the richness of the receptive fields of the last layer of feature maps; the residual shortcut connections, batch normalization layer and average pooling layer are used to replace the fully connected layer to improve training efficiency, and make the multi-scale feature learning ability more balanced at the same time. Two representative multi-scale defect data sets are used for experiments, and the experimental results verify the advancement and effectiveness of the proposed MSF-Net in the detection of surface defects with multi-scale features.
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Lu, Yuzhen, and Renfu Lu. "Detection of Surface and Subsurface Defects of Apples Using Structured- Illumination Reflectance Imaging with Machine Learning Algorithms." Transactions of the ASABE 61, no. 6 (2018): 1831–42. http://dx.doi.org/10.13031/trans.12930.
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Abstract. Machine vision technology coupled with uniform illumination is now widely used for automatic sorting and grading of apples and other fruits, but it still does not have satisfactory performance for defect detection because of the large variety of defects, some of which are difficult to detect under uniform illumination. Structured-illumination reflectance imaging (SIRI) offers a new modality for imaging by using sinusoidally modulated structured illumination to obtain two sets of independent images: direct component (DC), which corresponds to conventional uniform illumination, and amplitude component (AC), which is unique for structured illumination. The objective of this study was to develop machine learning classification algorithms using DC and AC images and their combinations for enhanced detection of surface and subsurface defects of apples. A multispectral SIRI system with two phase-shifted sinusoidal illumination patterns was used to acquire images of ‘Delicious’ and ‘Golden Delicious’ apples with various types of surface and subsurface defects. DC and AC images were extracted through demodulation of the acquired images and were then enhanced using fast bi-dimensional empirical mode decomposition and subsequent image reconstruction. Defect detection algorithms were developed using random forest (RF), support vector machine (SVM), and convolutional neural network (CNN), for DC, AC, and ratio (AC divided by DC) images and their combinations. Results showed that AC images were superior to DC images for detecting subsurface defects, DC images were overall better than AC images for detecting surface defects, and ratio images were comparable to, or better than, DC and AC images for defect detection. The ensemble of DC, AC, and ratio images resulted in significantly better detection accuracies over using them individually. Among the three classifiers, CNN performed the best, with 98% detection accuracies for both varieties of apples, followed by SVM and RF. This research demonstrated that SIRI, coupled with a machine learning algorithm, can be a new, versatile, and effective modality for fruit defect detection. Keywords: Apple, Defect, Bi-dimensional empirical mode decomposition, Machine learning, Structured illumination.
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Ibrahim, Eihab Abdelkariem Bashir, Ummi Raba'ah Hashim, Lizawati Salahuddin, Nor Haslinda Ismail, Ngo Hea Choon, Kasturi Kanchymalay, and Siti Normi Zabri. "Evaluation of texture feature based on basic local binary pattern for wood defect classification." International Journal of Advances in Intelligent Informatics 7, no. 1 (March 31, 2021): 26. http://dx.doi.org/10.26555/ijain.v7i1.393.
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Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects.
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Abid, Sabeur. "Texture defect detection by using polynomial interpolation and multilayer perceptron." Journal of Engineered Fibers and Fabrics 14 (January 2019): 155892501882527. http://dx.doi.org/10.1177/1558925018825272.
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This article deals with fabric defect detection. The quality control in textile manufacturing industry becomes an important task, and the investment in this field is more than economical when reduction in labor cost and associated benefits are considered. This work is developed in collaboration with “PARTNER TEXTILE” company which expressed its need to install automated defect fabric detection system around its circular knitting machines. In this article, we present a new fabric defect detection method based on a polynomial interpolation of the fabric texture. The different image areas with and without defects are approximated by appropriate interpolating polynomials. Then, the coefficients of these polynomials are used to train a neural network to detect and locate regions of defects. The efficiency of the method is shown through simulations on different kinds of fabric defects provided by the company and the evaluation of the classification accuracy. Comparison results show that the proposed method outperforms several existing ones in terms of rapidity, localization, and precision.
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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 (October 1, 2019): 4277. http://dx.doi.org/10.11591/ijece.v9i5.pp4277-4286.
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<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 factory. The proposed work includes two phases. The first phase is to detect the defects in fabrics. The second phase is the classification phase of the defect. At the defect detection stage, a Discrete Cosine Transfer (DCT) converts the images to the frequency domain. Image features then drawn and introduce them to the Elman Neural Network to detect the existence of defects. In the classification stage, the images are converted to the frequency domain by the Gabor filter and then the image features are extracted and inserted into the back propagation network to classify the fabric defects in those images. Fuzzy logic is then applied to neural network outputs and interference values are used in fuzzy logic to increase final discrimination. We evaluate a distinction rate of 91.4286% .After applying the fuzzy logic to neural network output; the discrimination rate was raised to 97.1428%. </p>
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Liang, Ying, Ke Xu, and Peng Zhou. "Mask Gradient Response-Based Threshold Segmentation for Surface Defect Detection of Milled Aluminum Ingot." Sensors 20, no. 16 (August 12, 2020): 4519. http://dx.doi.org/10.3390/s20164519.
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The surface quality of aluminum ingot is crucial for subsequent products, so it is necessary to adaptively detect different types of defects in milled aluminum ingots surfaces. In order to quickly apply the calculations to a real production line, a novel two-stage detection approach is proposed. Firstly, we proposed a novel mask gradient response-based threshold segmentation (MGRTS) in which the mask gradient response is the gradient map after the strong gradient has been eliminated by the binary mask, so that the various defects can be effectively extracted from the mask gradient response map by iterative threshold segmentation. In the region of interest (ROI) extraction, we combine the MGRTS and the Difference of Gaussian (DoG) to effectively improve the detection rate. In the aspect of the defect classification, we train the inception-v3 network with a data augmentation technology and the focal loss in order to overcome the class imbalance problem and improve the classification accuracy. The comparative study shows that the proposed method is efficient and robust for detecting various defects on an aluminum ingot surface with complex milling grain. In addition, it has been applied to the actual production line of an aluminum ingot milling machine, which satisfies the requirement of accuracy and real time very well.
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Hou, Wenhui, Dashan Zhang, Ye Wei, Jie Guo, and Xiaolong Zhang. "Review on Computer Aided Weld Defect Detection from Radiography Images." Applied Sciences 10, no. 5 (March 10, 2020): 1878. http://dx.doi.org/10.3390/app10051878.
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The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.
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Wan, Xiang, Xiangyu Zhang, and Lilan Liu. "An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets." Applied Sciences 11, no. 6 (March 15, 2021): 2606. http://dx.doi.org/10.3390/app11062606.
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The surface defects’ region of strip steel is small, and has various defect types and, complex gray structures. There tend to be a large number of false defects and edge light interference, which lead traditional machine vision algorithms to be unable to detect defects for various types of strip steel. Image detection techniques based on deep learning require a large number of images to train a network. However, for a dataset with few samples with category imbalanced defects, common deep learning neural network training tasks cannot be carried out. Based on rapid image preprocessing algorithms (improved gray projection algorithm, ROI image augmentation algorithm) and transfer learning theory, this paper proposes a set of processes for complete strip steel defect detection. These methods achieved surface rapid screening, defect feature extraction, sample dataset’s category balance, data augmentation, defect detection, and classification. Through verification of the mixed dataset, composed of the NEU surface dataset and dataset in this paper, the recognition accuracy of the improved VGG19 network in this paper reached 97.8%. The improved VGG19 network performs slightly better than the baseline VGG19 in six types of defects, but the improved VGG19 performs significantly better in the surface seams defects. The convergence speed and accuracy of the improved VGG19 network were taken into account, and the detection rate was greatly improved with few samples and imbalanced datasets. This paper also has practical value in terms of extending its method of strip steel defect detection to other products.
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Han, Young-Joo, and Ha-Jin Yu. "Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data." Applied Sciences 10, no. 7 (April 6, 2020): 2511. http://dx.doi.org/10.3390/app10072511.
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As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image classification. These methods are effective but require a large number of actual defect data. However, it is very difficult to get a large amount of actual defect data in industrial areas. To overcome this problem, we propose a method for defect detection using stacked convolutional autoencoders. The autoencoders we proposed are trained by using only non-defect data and synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts. A key advantage of our approach is that actual defect data is not required, and we verified that the performance is comparable to the systems trained using real defect data.
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Das, Hrishikesh, Swapna Sunkari, Joshua Justice, Helen Pham, and Kyeong Seok Park. "Effect of Defects in Silicon Carbide Epitaxial Layers on Yield and Reliability." Materials Science Forum 963 (July 2019): 284–87. http://dx.doi.org/10.4028/www.scientific.net/msf.963.284.
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Inline metrology tools are widely used to detect defects in SiC epitaxial layers. The defect statistics are used in a variety of ways to determine quality, pass/fail and screen affected die. In this work, we document the automated detection and classification of various epitaxial defects based on type and origin. We further classify these categories into killer and non-killer defects and compare them to the electrical yield of Schottky Diodes. The origins of these defects are determined in broad categories, resulting in a clustering and yield-scaling model, which agrees very closely to experimental data. Further, we look at on-wafer screening techniques of potential weak die by both defect tagging and unclamped inductive switching (UIS) stress testing. Successful 1000-hr reliability tests show the robustness of our detection and screening methods.
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Daniel, Jackson, A. Abudhahir, and J. Janet Paulin. "Tsallis Entropy Segmentation and Shape Feature-based Classification of Defects in the Simulated Magnetic Flux Leakage Images of Steam Generator Tubes." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 01 (May 29, 2019): 2054002. http://dx.doi.org/10.1142/s0218001420540026.
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Early detection of water or steam leaks into sodium in the steam generator units of nuclear reactors is an important requirement from safety and economic considerations. Automated defect detection and classification algorithm for categorizing the defects in the steam generator tube (SGT) of nuclear power plants using magnetic flux leakage (MFL) technique has been developed. MFL detection is one of the most prevalent methods of pipeline inspection. Comsol 4.3a, a multiphysics modeling software has been used to obtain the simulated MFL defect images. Different thresholding methods are applied to segment the defect images. Performance metrics have been computed to identify the better segmentation technique. Shape-based feature sets such as area, perimeter, equivalent diameter, roundness, bounding box, circularity ratio and eccentricity for defect have been extracted as features for defect detection and classification. A feed forward neural network has been constructed and trained using a back-propagation algorithm. The shape features extracted from Tsallis entropy-based segmented MFL images have been used as inputs for training and recognizing shapes. The proposed method with Tsallis entropy segmentation and shape-based feature set has yielded the promising results with detection accuracy of 100% and average classification accuracy of 96.11%.
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Liu, Shengbo, Pengyuan Fu, Lei Yan, Jian Wu, and Yandong Zhao. "Detection of Surface Defects in Logs Using Point Cloud Data and Deep Learning." International Journal of Circuits, Systems and Signal Processing 15 (July 19, 2021): 607–16. http://dx.doi.org/10.46300/9106.2021.15.67.
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Deep learning classification based on 3D point clouds has gained considerable research interest in recent years.The classification and quantitative analysis of wood defects are of great significance to the wood processing industry. In order to solve the problems of slow processing and low robustness of 3D data. This paper proposes an improvement based on littlepoint CNN lightweight deep learning network, adding BN layer. And based on the data set made by ourselves, the test is carried out. The new network bnlittlepoint CNN has been improved in speed and recognition rate. The correct rate of recognition for non defect log, non defect log and defect log as well as defect knot and dead knot can reach 95.6%.Finally, the "dead knot" and "loose knot" are quantitatively analyzed based on the "integral" idea, and the volume and surface area of the defect are obtained to a certain extent,the error is not more than 1.5% and the defect surface reconstruction is completed based on the triangulation idea.
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Yang, Yi, Eric Maggard, Yousun Bang, Minki Cho, and Jan P. Allebach. "Banding defect detection and image quality classification." Electronic Imaging 2021, no. 16 (January 18, 2021): 245–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.16.color-245.
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Banding has been regarded as one of the most severe defects affecting the overall image quality in the printing industry. There has been a lot of research on it, but most of them focused on uniform pages or specific test images. Aiming at detecting banding on customer’s content pages, this paper proposes a banding processing pipeline that can automatically detect banding, identify periodic and isolated banding, and estimate the periodic interval. In addition, based on the detected banding characteristics, the pipeline predicts the overall quality of printed customer’s content pages and obtains predictions similar to human perceptual assessment.
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Bukalov, G. K., A. O. Burygin, I. G. Panin, and A. B. Tortsev. "Defect Detection Using FCN Modification for Finding Rare Defects on Large Areas." INFORMACIONNYE TEHNOLOGII 26, no. 12 (December 15, 2020): 683–87. http://dx.doi.org/10.17587/it.26.683-687.
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here is problem of finding defects on a textile sling on large areas. For this purpose, the image goes through several stages: creation of a convolutional U-Net network, extraction of U-Net features, classification by the Random Forest algorithm, and identification of defective areas via MSER. The Random Forest classifier is used to segment parts of the input image obtained from U-Net. Computational experiments were conducted to study the effectiveness of the proposed method in comparison with existing methods.
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Ge, Jiuhao, Chenkai Yang, Ping Wang, and Yongsheng Shi. "Defect Classification Using Postpeak Value for Pulsed Eddy-Current Technique." Sensors 20, no. 12 (June 16, 2020): 3390. http://dx.doi.org/10.3390/s20123390.
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In this paper, a feature termed as the postpeak value is proposed for the pulsed eddy current technique (PECT). Moreover, a method using the postpeak value is proposed to classify surface and reverse defects. A PECT system is built for verification purposes. Experiment results prove that the postpeak feature value has better performance than that of the traditional peak value in the case of reverse defect detection. In contrast, the peak value is better than the postpeak value in the case of surface defect detection. Experiment results also validate that the proposed classification algorithm has advantages: classification can be achieved in real time, the calculation process and results are easy to understand, and supervised training is unnecessary.
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Shankar, N. G., Z. W. Zhong, and N. Ravi. "Classification of Defects on Semiconductor Wafers Using Priority Rules." Defect and Diffusion Forum 230-232 (November 2004): 135–48. http://dx.doi.org/10.4028/www.scientific.net/ddf.230-232.135.
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This paper presents a template-based vision system to detect and classify the nonuniformaties that appear on the semiconductor wafer surfaces. Design goals include detection of flaws and correlation of defect features based on semiconductor industry expert’s knowledge. The die pattern is generated and kept as the reference beforehand from the experts in the semiconductor industry. The system is capable of identifying the defects on the wafers after die sawing. Each unique defect structure is defined as an object. Objects are grouped into user-defined categories such as chipping, metallization peel off, silicon dust contamination, etc., after die sawing and micro-crack, scratch, ink dot being washed off, bridging, etc., from the wafer. This paper also describes the vision system in terms of its hardware modules, as well as the image processing algorithms utilized to perform the functions.