A New Defect Classification Approach Based on the Fusion Matrix of Multi-Eigenvalue

Defect classification is an important part of automated surface defect detection systems. Generally, the types of surface defects of industrial products are complex and diverse, and the correct defect type classification can help to subsequently extract the characteristic of defects. Moreover, defect classification can help to count the number of defect modes automatically and provide data support for the precise maintenance of product production. Due to the large number of surface defects and the small difference between defect types, traditional classification methods are difficult to classify defect accurately. Therefore, in order to improve the accuracy of defect image classification, this paper proposes a defect image classification method based on transfer learning and sparse coding. Firstly, a deep CNN feature extraction algorithm for defect images is proposed in combination with transfer learning. Then, the deep CNN features of the defect image are dimension-reduced and sparsely optimized using the sparse coding techniques, and the sparse CNN features are obtained. Finally, the sparse CNN features are classified to realize the defect type determination using the linear SVM. The accuracy of the proposed method is verified by using a steel surface defect image benchmark database, and the effectiveness of the proposed method is proved.

Quality control in metal product manufacturing relies heavily on accurately detecting and classifying surface defects through visual inspection. Recently, convolutional neural networks (CNNs) have shown promising results in automating this process with high accuracy. This research paper proposes a new (experimental version) Lite Convolutional Neural Network (LCNN) designed to analyze image data to detect and classify surface defects on metallic surfaces. Our model was trained on a metal surface defects dataset comprising 1800 images of six different types of surface defects. Despite using relatively small datasets, the proposed LCNN version achieves a classification accuracy of 91.67%, highlighting its effectiveness in real-world defect detection scenarios.

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.

Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects’ patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods.

Carbon fiber reinforced polymer (CFRP) has become an essential structural material in aerospace, new energy, rail transportation, and other fields due to its lightweight and high-strength properties. However, its complex manufacturing processes and demanding service environments can lead to various types of damage on both the surface and inside the material, significantly compromising safety. Existing detection techniques often adopt a step-by-step inspection mode, which results in complex processes and makes it difficult to achieve cross-dimensional correlation analysis of defects, leading to blind spots in structural health assessment. To address these issues, this paper proposes a method for simultaneous detection of surface and subsurface defects in CFRP based on infrared polarization imaging. First, we developed an infrared polarization imaging-based defect detection system and collected a CFRP defect dataset of samples containing various surface and subsurface defects. Then, we proposed a surface and subsurface defect detection network, which integrates extracted thermal diffusion features and polarization features to decouple and enhance surface and subsurface features. Through the constraints of multi-task defect detection, simultaneous detection of surface and subsurface defects is achieved. The experiments on the collected CFRP defect dataset demonstrate the effectiveness of the proposed method.

Cylinder liner is an internal part of the automobile engine, which plays an important role in the automobile internal combustion engine. Therefore, it is a top priority to accurately and quickly detect the cylinder liner surface defects. In order to effectively achieve the classification and localization of surface defects on the cylinder liner, this paper establishes a dataset for surface defects on cylinder liner and proposes a based on improved YOLOv5 algorithm for detecting surface defects on cylinder liner. Firstly, a machine vision system is established to acquire on-site images and perform manual annotation to build the dataset of surface defects on cylinder liner. Secondly, the GSConv SlimNeck mechanism is introduced to reduce the model complexity; the Bi-directional Feature Pyramid Network (BiFPN) is used to fuse the feature information at different scales to enhance the detection accuracy of small surface defects on cylinder liner; and embedding the SimAM attention mechanism to focus on the object region of interest and improve the accuracy and robustness of the model. The final improved YOLOv5 model reduces the number of model parameters by 15.8% compared to the non-improved YOLOv5. And the experimental results on our self-built dataset for cylinder liner defects show that the mAP0.5 is improved by 0.4%. This means that the accuracy of model detection was not compromised. This method can be applied to actual production processes.

FaNet: Feature-aware network for few shot classification of strip steel surface defects

A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.

Surface defects on metallic and non-metallic components can have serious effects on the reliability of the complete system. Typical examples are pistons with surface defects that cause scratching of the cylinder surface. For this reason it is of utmost importance to detect such defects as early as possible in the production process. So far, no real time solutions exist that fully satisfy industrial requirements in terms of speed, accuracy and reliability. Hence it is common to use visual inspection by humans which is error prone and causes additional personal costs. In this paper a new approach to obtain real time behaviour of industrial image processing systems by using multivariate techniques is presented. This methodology is originally used in chemometrics for statistical evaluation of measurement data and is now applied to image processing to take advantage of the high numerical efficiency of the underlying mathematics. Multivariate techniques can be applied to both the problem of automatic identification and classification of surface defects with digital images. The key to the envisaged real time ability is the high numerical efficiency of the proposed multivariate method. It manages defect detection with vector/matrix multiplication only - no calculation of powers or exponential functions is required. This enables efficient real time implementations on DSP platforms which are profiled for this type of calculations.

A new graph-based semi-supervised method for surface defect classification

The classification of steel surface defects plays a very important role in analyzing their causes to improve manufacturing process and eliminate defects. However, defective samples are very scarce in actual production, so using very few samples to construct a good classifier is a challenge to be addressed. If the layer number of the model with proper depth is increased, the model accuracy will decrease (not caused by overfit), and the training error as well as the test error will be very high. This is called the degradation problem. In this paper, we propose to use feature extraction + feature transformation + nearest neighbors to classify steel surface defects. In order to solve the degradation problem caused by network deepening, the three feature extraction networks of Residual Net, Mobile Net and Dense Net are designed and analyzed. Experiment results show that in the case of a small sample number, Dense block can better solve the degradation problem caused by network deepening than Residual block. Moreover, if Dense Net is used as the feature extraction network, and the nearest neighbor classification algorithm based on Euclidean metric is used in the new feature space, the defect classification accuracy can reach 92.33% when only five labeled images of each category are used as the training set. This paper is of some guiding significance for surface defect classification when the sample number is small.

The quality of smooth technical surfaces is characterized by the surface roughness and by surface defects. For a stable quality control, objective measuring conditions are necessary. The objective measurement of surface defects especially is an unsolved problem. For the investigation of the scattering characteristics of different surface defects, a measuring device was developed. With this measuring device, the type of defect and the angle dependent scattering intensity were measured and compared with theoretical calculations.

A nondestructive testing method for detecting surface defects of Si3N4-Bearing cylindrical rollers based on an optimized convolutional neural network

Majority of surface and subsurface defects in the continuously cast steel may be attributed to surface standing waves and turbulence. A lot of work has been done on surface defects and on understanding the behavior of the surface waves but there is a dearth of literature connecting the two. Also 3-D modeling of surface waves with heat transfer and solidification has received little attention. In the present study, a 3-dimensional mathematical model incorporating turbulent fluid flow, heat transfer, and solidification using a commercial code, FLOW3D, was developed. Using the model, the applicability of Froude criterion for scaling down industrial flow systems was analyzed. Although the small scale models, using this criterion, correctly predict the general fluid flow but the prediction of surface waves is not very accurate. Also, the effect of temperature is difficult to incorporate in water models. The model was used to study the effect of temperature on surface waves and verify the water modeling results. The effects of casting and SEN parameters on the fluid flow and surface waves were studied.

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