Query: surface defect, neural network

title: A semi supervised convolutional neural network based method for steel surface defect recognition
abstract: Automatic defect recognition is one of the research hotspots in steel production, but most of the current methods focus on supervised learning, which relies on large scale labeled samples. In some real world cases, it is difficult to collect and label enough samples for model training, and this might impede the application of most current works. The semi supervised learning, using both labeled and unlabeled samples for model training, can overcome this problem well. In this paper, a semi supervised learning method using the convolutional neural network (CNN) is proposed for steel surface defect recognition. The proposed method requires fewer labeled samples, and the unlabeled data can be used to help training. And, the CNN is improved by Pseudo Label. The experimental results on a benchmark dataset of steel surface defect recognition indicate that the proposed method can achieve good performances with limited labeled data, which achieves an accuracy of 90.7% with 17.53% improvement. Furthermore, the proposed method has been applied to a real world case from a Chinese steel company, and obtains an accuracy of 86.72% which significantly better than the original method in this workshop.

title: Solar cell surface defect inspection based on multispectral convolutional neural network
abstract: Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex background is a challenge of solar cell manufacturing. The traditional manufacturing process relies on human eye detection which requires a large number of workers without a stable and good detection effect. In order to solve the problem, a visual defect detection method based on multi spectral deep convolutional neural network (CNN) is designed in this paper. Firstly, a selected CNN model is established. By adjusting the depth and width of the model, the influence of model depth and kernel size on the recognition result is evaluated. The optimal CNN model structure is selected. Secondly, the light spectrum features of solar cell color image are analyzed. It is found that a variety of defects exhibited different distinguishable characteristics in different spectral bands. Thus, a multi spectral CNN model is constructed to enhance the discrimination ability of the model to distinguish between complex texture background features and defect features. Finally, some experimental results and K fold cross validation show that the multi spectral deep CNN model can effectively detect the solar cell surface defects with higher accuracy and greater adaptability. The accuracy of defect recognition reaches 94.30% Applying such an algorithm can increase the efficiency of solar cell manufacturing and make the manufacturing process smarter.

title: A Weakly Supervised Surface Defect Detection Based on Convolutional Neural Network
abstract: Surface defect detection is a critical task in product quality assurance for manufacturing lines. The deep learning based methods recently developed for defect detection are typically trained using a supervised learning strategy and large defect sample sets. Conventional methods often require additional pixel level labeling or bounding boxes to predict the location of defects. However, the number of required samples and the time intensive annotation process limits the practical use of these algorithms. As such, this study proposes a weakly supervised detection framework in which a CNN model is trained to identify surface cracks in motor commutators. The model was trained using small subsets of defect samples ~5 30) and does not require a pre trained network. This approach consists of localization and decision networks that simultaneously predict both the location and probability of defects. A new loss function was also developed to identify abnormal regions in a sample with accessible image level labels. A collaboration learning strategy was then applied to utilize the loss function and compensate for imbalances at the pixel level. Experimental results using a small number of image level training labels from a real industrial dataset exhibited a 99.5% recognition accuracy, which is comparable to relevant methods using pixel level labels.

title: A surface defect detection method of the magnesium alloy sheet based on deformable convolution neural network
abstract: S. Y. Guan, W. Y. Zhang, Y. F. Jiang, School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China.  W. Y. Zhang (zhangwenyu8518@126. com) In the rolling process of the magnesium alloy sheet, due to improper control parameters or inaccurate production equipment and other reasons, the surface of the magnesium alloy sheet is prone to appearance of edge crack, fold, inclusion, ripple, scratch and other defects. In order to improve the surface quality of the magnesium alloy sheet, a surface defect detection method based on deformable convolution neural network is proposed in the paper, which presents a higher detection accuracy than those traditional methods on the convolutional neural network (CNN) support vector machine (SVM) and Bayes. The experiment result shows the final detecting accuracy is greater than 95

title: Knowledge Graph guided Convolutional Neural Network for Surface Defect Recognition
abstract: Automatic surface defect recognition is very important to ensure product quality in modern manufacturing. In recent studies, deep learning based methods develop rapidly because of its end to end training, but the difficult in collecting massive samples limits the application of these methods. Due to the intra class variations and inter class similarities in surface defect, it is difficult for the methods to extract features precisely. The knowledge graph can represent the relations of defect samples to improve the ability of extracting features. Therefore, this paper proposes a knowledge graph guided convolutional neural network (KCNN) for surface defect recognition. Firstly, KCNN defines a knowledge graph by computing cosine distance of defect samples, which can represent the relations of defect samples. Secondly, KCNN uses the knowledge graph to define an extra cost function to guide the training of CNN, which can improve the abilities of extracting features of defect samples. The proposed method is tested on a benchmark dataset and the results show that KCNN achieves as high as 99.79% of the prediction accuracy on small sample training dataset (300 samples) without increasing the recognition time. Compared with other deep learning methods, KCNN achieves the best performances.

title: Deep Regression Neural Network for Industrial Surface Defect Detection
abstract: Industrial product surface defect detection is very important to guarantee high product quality and production efficiency. In this work, we propose a regression and classification based framework for generic industrial defect detection. Specifically, the framework consists of four modules: deep regression based detection model, pixel level false positive reduction, connected component analysis and deep network for defect type classification. To train the detection model, we propose a high performance deep network structure and an algorithm to generate label data to capture the defect severity information from data annotation. We have tested the method on two public benchmark datasets, AigleRN and DAGM2007, and an in house capacitor image dataset. The results have shown that our method can achieve the state of the art performance in terms of detection accuracy and efficiency.

title: A Compact Convolutional Neural Network for Surface Defect Inspection
abstract: The advent of convolutional neural networks (CNNs) has accelerated the progress of computer vision from many aspects. However, the majority of the existing CNNs heavily rely on expensive GPUs (graphics processing units) to support large computations. Therefore, CNNs have not been widely used to inspect surface defects in the manufacturing field yet. In this paper, we develop a compact CNN based model that not only achieves high performance on tiny defect inspection but can be run on low frequency CPUs (central processing units) Our model consists of a light weight (LW) bottleneck and a decoder. By a pyramid of lightweight kernels, the LW bottleneck provides rich features with less computational cost. The decoder is also built in a lightweight way, which consists of an atrous spatial pyramid pooling (ASPP) and depthwise separable convolution layers. These lightweight designs reduce the redundant weights and computation greatly. We train our models on groups of surface datasets. The model can successfully classify/segment surface defects with an Intel i3 4010U CPU within 30 ms. Our model obtains similar accuracy with MobileNetV2 while only has less than its 1/3 FLOPs (floating point operations per second) and 1/8 weights. Our experiments indicate CNNs can be compact and hardware friendly for future applications in the automated surface inspection (ASI)

title: A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection
abstract: Surface defect detection is an indispensable step in the production process. Recent researches based on deep learning have paid primarily attention to improving accuracy. However, it is difficult to apply in real situation, because of huge number of parameters and the strict hardware requirements. In this paper, a lightweight fully convolutional neural network, named LFCSDD, is proposed. The parameters of our model are 11x fewer than baselines at least, and obtain the accuracy of 99.72% and 98.74% on benchmark defect datasets, DAGM 2007 and KolektorSDD, respectively, outperforming all the baselines. In addition, our model can process the images with different sizes, which is verified on the RSDDs with the accuracy of 97.00%

title: Convolutional Neural Network for Wafer Surface Defect Classification and the Detection of Unknown Defect Class
abstract: An automatic defect classification (ADC) system identifies and classifies wafer surface defects using scanning electron microscope images. By classifying defects, manufacturers can determine whether the wafer can be repaired and proceed to the next fabrication step. Current ADC systems have high defect detection performance. However, the classification power is poor. In most work sites, defect classification is performed manually using the naked eye, which is unreliable. This paper proposes an ADC method based on deep learning that automatically classifies various types of wafer surface damage. In contrast to conventional ADC methods, which apply a series of image recognition and machine learning techniques to find features for defect classification, the proposed model adopts a single convolutional neural network (CNN) model that can extract effective features for defect classification without using additional feature extraction algorithms. Moreover, the proposed method can identify defect classes not seen during training by comparing the CNN features of the unseen classes with those of the trained classes. Experiments with real datasets verified that the proposed ADC method achieves high defect classification performance.

title: Steel Surface Defect Detection Using Convolutional Neural Network
abstract: Steel is the most important engineering and construction material in the world. It is used in all aspects of our lives. But as every metal is can be defected and then will not be useful by the consumer Steel surface inspection has seen an important attention in relation with industrial quality of products. In addition, it has been studied in different methods based on image classification in the most of time, but these can detect only such kind of defects in very limited conditions such as illumination, obvious contours, contrast and noise..etc. In this paper, we aim to try a new method to detect steel defects this last depend on artificial intelligence and artificial neural networks. We will discuss the automatic detection of steel surface defects using the convolutional neural network, which can classify the images in their specific classes. The steel we are going to use will be well classified weather the conditions of imaging are not the same, and this is the advantage of the convolutional neural network in our work. The accuracy and the robustness of the results are so satisfying.