A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM

Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy / incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MC- SVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 % error margin. The method also outperforms other soft computing methods proposed in literature.

Mechanical parts often have surface defects such as scratches, cracks and pits during the production process, which seriously affect product quality and service life. To address this challenge, this paper proposes a method for detecting and quantifying surface defects of mechanical parts based on image processing. First, this paper uses high-resolution image acquisition technology to obtain surface images of mechanical parts, and improves image quality through preprocessing steps. Then, this paper uses edge detection, morphological operations, and image segmentation algorithms to extract the features of surface defects, thereby realizing automatic identification of defective areas. Finally, this paper combines the Faster R-CNN (Faster Region-Based Convolutional Neural Network) algorithm to further refine the classification and quantification of defects and obtain the specific location, size and type of defects. Experimental results show that among the 500 mechanical parts samples tested, the proposed method has a mean average precision (mAP) of 0.93 in surface defect detection. Compared with other image processing methods, this method has significantly improved defect detection accuracy and speed. Especially when processing higher-resolution images, the Faster R-CNN model can achieve efficient defect classification and quantification to meet the needs of real-time detection in industrial production.

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning

Using deep learning to identify the depth of metal surface defects with narrowband SAW signals

Machine vision systems with effective image processing methods are used in quality grading of agricultural products. A pattern recognition technique was developed to detect and classify surface defects such as pitting, splitting and stem-end rot found in images of mandarin fruits. The developed technique employs fuzzy thresholding for image segmentation, binary wavelet transform (BWT) for feature extraction and a rule based linear classifier model for detection and classification of the defects. The moment invariants computed from the detail subimage of BWT were taken as feature values. This paper in detail describes about the pattern recognition algorithm and its implementation. The detection and classification results obtained from the algorithm are reported and discussed.

In this paper, we present an evaluation of face recognition algorithm in video surveillance system using multi-class Support Vector Machine (SVM). We also show how the variation of sizes (due to distance) and intensity of training images (due to camera hardware) can improve the face recognition rate. The system starts by detecting the face regions using AdaBoost training algorithm. Holistic approach is chosen to extract features from the detected face regions as this approach is the simplest. After the feature vectors are generated, multi-class SVM is used for classification step. SVM is chosen as the classifier as it is a low samples, high dimensional classifier. As the training and test set, we use SCface database in which the images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Based on our study using only linear kernel multi-class SVM, it is shown that distance of approximately 2.6m gives the best results, and the training and test images have to be from similar intensity cameras although surveillance camera will have different quality of images.

Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution and can be used to discover a unique pattern of students' academic performance. Secondary schools are increasing rapidly from past years and progress of the institution can be measured based on student's success and failure rates. Failure rates can be measured in terms of a core subject such as mathematics which has been considered in this proposed system. Real data was collected using school reports and questionnaire method by the Portugal school which has been used for the project. The students will be classified according to the grades assigned for the range of marks scored by them. This involves classifying the students into five levels of grading system starting from grade ‘A’ to grade ‘F’ where grade ‘A’ represents the student getting the highest marks, grade ‘B’ being the second highest, grade ‘C’ being third, grade ‘D’ being the fourth and the grade ‘F’ which implies that the student has failed. To carry out this type of classification many machine learning algorithms can be used to implement it. Comparison was made between the algorithms like Multiclass Support Vector Machine and Neural Networks using the Weka tool. Based on the analysis carried out Multiclass Support Vector Machine showed prominent accuracy. The Multiclass Support Vector Machine is implemented on the basis of one-to-rest strategy use of class labels which is primarily an extension of linear Support Vector Machine. In order to get appropriate results in terms of the accuracy of the model, parameters like ‘C’ and ‘Gamma’ is tuned while implementing Multiclass Support Vector Machine. The result gives a good predictive accuracy based on the grades that are provided by the school. The accuracy of prediction made by Support Vector Machine Classifier is determined by using K-fold cross validation, according to which the accuracy is 89%.

This article presents a novel text categorization framework based on Support Vector Machine (SVM) and SMTP similarity mea-sure. The performance of the SVM mainly depends on the selection of kernel function and soft margin parameter C. To reduce the impact of kernel function and parameter C, in this article, a novel text categorization framework called SVM-SMTP framework is developed. In the proposed SVM-SMTP framework, we used Similarity Measure for Text Processing (SMTP) measure in place of optimal separating hyper-plane as categorization decision making function. To assess the efficacy of the SVM-SMTP framework, we carried out experiments on publically available datasets: Reuters-21578 and 20-NewsGroups. We compared the results of SVM-SMTP framework with other four similarity measures viz., Euclidean, Cosine, Correlation and Jaccard. The experimental results show that the SVM-SMTP framework outperforms the other similarity measures in terms of categorization accuracy.

The design and study of pulsed eddy current sensors for detecting surface defects in small-diameter rods are highly significant. Accurate detection and identification of surface defects in small-diameter rods may be attained by the ongoing optimization of sensor design and enhancement of detection technologies. This article presents the construction of a non-coaxial differential eddy current sensor (Tx-Rx sensor) and examines the detection of surface defects in a small diameter bar. A COMSOL 3D model is developed to examine the variations in eddy current distribution and defect signal characteristics between the plate and rod components. The position of the excitation coil on the bar and the eddy current disruption around the defect are examined. Additionally, a Tx-Rx sensor has been developed and enhanced concerning coil dimensions, coil separation, and elevation height. An experimental system is established to detect bar structures with surface defects of varying depths, and a model correlating differential signal attenuation with defect depth is proposed, achieving a quantitative relative error of less than 5%, thereby offering a reference for the quantitative detection of bar surface defects.

This article presents improvements in the segmentation module, feature extraction module, and the classification module of a low-cost automated visual inspection (AVI) system for wood defect classification. One of the major drawbacks in the low-cost AVI system was the erroneous segmentation of clear wood regions as defects, which then introduces confusion in the classification module. To reduce this problem, we use the fuzzy min–max neural network for image segmentation (FMMIS). The FMMIS method grows boxes from a set of seed pixels, yielding ideally the minimum bounded rectangle for each defect present in the wood board image. Additional features with texture information are considered for the feature extraction module, and multi-class support vector machines are compared with multilayer perceptron neural networks in the classification module. Results using the FMMIS, additional features, and a pairwise classification support vector machine on a 550 test wood image set containing 11 defect categories show 91% of correct classification, which is significantly better than the original 75% of the low-cost AVI system. The use of computational intelligence techniques improved significantly the overall performance of the proposed automated visual inspection system for wood boards.

Pipeline abnormal classification based on support vector machine using FBG hoop strain sensor

Face recognition (FR) has drawn considerable interest and attention in the area of pattern recognition. FR is still a challenging task in real time applications even though they are a number of face recognition algorithms which are available and work in various constrained environment. The paper proposes a FR algorithm using Modified Local Ternary Pattern (MLTP) with multi class Support Vector Machine (SVM) classifier. The MLTP features of the face images are classified by an Error-Correcting Output Code (ECOC) multiclass model with SVM. The proposed method is tested on six standard face databases. The experimental results have been demonstrated that the performance of MLTP with SVM can achieve higher recognition accuracy compared to the conventional methods.

Electrolytic capacitor surface defect detection based on deep convolution neural network

Automatic Speech Recognition (ASR) is essentially a problem of pattern classification, however, the time dimension of the speech signal has prevented to pose ASR as a simple static classification problem. Support Vector Machine (SVM) classifiers could provide an appropriate solution, since they are very well adapted to high-dimensional classification problems. Nevertheless, the use of SVMs for ASR is by no means straightforward, mainly because SVM classifiers require an input of fixed-dimension. In this paper we study the use of a HMM-based segmentation as a mean to get the fixed-dimension input vectors required by SVMs, in a problem of isolated-digit recognition. Different configurations for all the parameters involved have been tested. Also, we deal with the problem of multi-class classification (as SVMs are initially binary classifers), studying two of the most popular approaches: 1-vs-all and 1-vs-1.