Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning

The wood-based furniture manufacturing industries prioritize quality of production to meet higher market demands. Identifying various types of edge-glued wooden panel defects are a challenge for a human worker or a camera. Several studies have shown that the detection of edge-glued defects with low, high, normal, overlong, short is identified but detection of residue and bluntness is highly challenging. Thus, the present model identifies defects of low, high, normal, overlong, short by computer vision and/or deep learning, whereas defects of residue and bluntness by deep learning based decide by pass for having better performance. The goal of this paper is to provide an improved defect detection solution for wood-based furniture manufacturing industries by process automation. Therefore, a system was designed that takes defect input images from a camera as raw image and laser-aligned image for defect detection of the edge-glued wooden panel. The process automation then performs computer vision-based image features extraction with deep learning for defect detection. The aim of this paper is to solve edge-glued defect detection problems by using design and implementation of edge-glued wooden defect detection, that can be stated as edge-glued wooden panel defect detection using deep learning (WDD-DL) for process automation by artificial intelligence and Automated Optical Inspection (AOI) consolidation. Possibly there exist several types of defects on the edges while edge-banding on the wooden panel in furniture manufacturing. Therefore, the scope is to achieve higher accuracy by raw image and laser-aligned image feature extraction using deep learning algorithms for final result defect classification in WDD-DL by AOI. The WDD-DL system uses Gabor, Harris corner, morphology, structured light detection and curvature calculation for pre-processing and InceptionResnetV2 Convolutional Neural Network algorithm to attain the best results. The applications of this work can be found in quality control of the furniture manufacturing industry for an edge, corner, joint defect detection of the wooden panels. The WDD-DL achieves best results as the precision, recall and F1 score are 0.97, 0.90 and 0.92, respectively. The experiments demonstrate higher accuracy achievement as compared to other methods with overkill and escape rate analysis. Ultimately, the discussion section provides an interesting experience sharing about the necessary factors for implementing the WDD-DL in real-time industrial operations.

In the machining of precision components, a range of defects, including scratches, pits and uneven materials, will inevitably manifest on the surface of the components. Such defects not only result in a reduction in the quality and precision of the parts, but also have an impact on the overall quality, performance, and longevity of the equipment. It is therefore of particular importance to implement effective detection and repair procedures for surface defects in parts. The implementation of efficient detection methods enables the identification and rectification of defects in a prompt and effective manner, thereby ensuring the reliability and long-term stability of the equipment. In this work, we propose a deep neural network-based model that utilises deep convolutional neural networks (DCNNs) to extract features on the surface of parts, and gradually extracts the spatial and textural features of images through multiple convolutional layers and pooling layers. Subsequently, the aforementioned features are fed into the fully connected layer, where complex feature representations are further learned. Finally, the accurate classification of defect types is achieved through multilayer neural networks. The model is thus capable of effectively capturing and identifying different types of surface defects, thereby improving the accuracy and efficiency of identification. The experimental analysis demonstrates that the model exhibits high accuracy and robustness in the identification of different types of defects, thereby verifying its effectiveness and reliability in practical applications.

In the actual industrial production environment, the surface defects of products are subtle, and the number of different types of defect data samples is also quite small. Most deep learning models rely on a large number of training samples and parameters to achieve high-precision defect detection. At the same time, the edge computing layer in the actual industrial environment may also encounter transmission delays and insufficient resources. Training a proper model for a specific type of surface defect while simultaneously satisfying the real-time accuracy of defect detection is still a challenging task. To effectively deal with the above challenges, we propose an edge-cloud computing defect detection model based on the intrinsic mean feature detector in the Lie Group space. The modules in the model adopt a symmetrical structure, which can extract related features more effectively. Different from existing models, this model utilizes the Lie Group space intrinsic mean feature as a metric to characterize the essential attributes of different types of surface defects. In addition, we propose an intrinsic mean attention mechanism in the Lie Group manifold space that is easy to implement at the edge service layer without increasing the number of model parameters, thereby enhancing the detection performance of tiny surface defects. Extensive experiments on three publicly available and challenging datasets reveal the superiority of our model in terms of detection accuracy, real-time detection, number of parameters, and computational performance. In addition, our proposed model also shows competitiveness and advantages compared with state-of-the-art models.

Health state estimation of cutting tools is a key component for maintaining the reliability and stability of the manufacturing process. Although deep learning (DL) based tool wear indirect monitoring methods have been presented, most of them require a substantial amount of data to avoid high misclassification rates. To this end, this article proposes a novel estimation approach based on data-enhanced deep transfer learning (TL) methodology, which integrates multivariate data enhancements, deep convolution network, and generative adversarial learning for state assessment of the tool wear under small sample size. The proposed method includes three innovative points: 1) a multi-step data enhancement method is employed to augment the wavelet scalogram data as model input; 2) a novel training process is adopted to obtain optimal hyperparameters of TL-AlexNet and TL-VGGNet-16 model; 3) a deep transfer learning model for the enhanced data is presented to adaptively estimate the health state of the cutting tools. The proposed approach only requires a small quantity of data to achieve satisfactory estimation performance compared with other DL methods through experimental validation, and the average prediction accuracy for each stage is greater than 95%, which demonstrates the effectiveness and superiority of the presented model.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Ultrasonic guided waves are sensitive to many different types of defects and have been studied for defect recognition in rail. However, most fault recognition algorithms need to extract features from the time domain, frequency domain, or time-frequency domain based on experience or professional knowledge. This paper proposes a new method for identifying many different types of rail defects. The segment principal components analysis (S-PCA) is developed to extract characteristics from signals collected by sensors located at different positions. Then, the Support Vector Machine (SVM) model is used to identify different defects depending on the features extracted. Combining simulations and experiments of the rails with different kinds of defects are established to verify the effectiveness of the proposed defect identification techniques, such as crack, corrosion, and transverse crack under the shelling. There are nine channels of the excitation-reception to acquire guided wave detection signals. The results show that the defect classification accuracy rates are 96.29% and 96.15% for combining multiple signals, such as the method of single-point excitation and multi-point reception, or the method of multi-point excitation and reception at a single point.

Eddy current testing is a nondestructive testing method, which is used to detect discontinuities and defects in conductive materials. Using this technique, two different types of artificial defects in a railhead were evaluated in order to analyze the relationship between different types of defects and eddy current signals, and to obtain data on the size of the rail surface defects and crack location. The actually used rail sample was also studied. Surface cracks and defects were clearly observed as amplitude and phase changes of detected signals. This study succeeds in quantitatively analyzing and discriminating the damage types.

Sanitary ceramic products, such as toilet and wash basin, are widely used in our daily life. Sanitary ceramics are expected to have some excellent physical properties, such as corrosion resistance, easy cleaning, and low water absorption. However, surface defects in sanitary ceramics are inevitable due to complex production processes and changing production environment. Therefore, surface defect detection must be performed in the manufacturing process of sanitary ceramics. There are many types of surface defects in sanitary ceramics, and different types of defects have large differences in characteristics and scales. Traditional detection methods with artificially designed features and classifiers are difficult to apply in this context. In addition, there are few studies on surface defect detection methods of sanitary ceramics based on deep neural networks. In this article, a lightweight real-time defect detection network based on the lightweight backbone MobileNetV3 is presented. The proposed network achieves multi-scale detection of surface defects in sanitary ceramics with a multi-layer feature pyramid. Combining region proposal network and anchor-free method, real-time defect detection is achieved. Finally, a detection head with channel attention structure and a low-level mixed feature classification strategy is used to perform defect classification with higher accuracy. Experimental results show that the proposed approach achieves at least 22.9% detection speed improvement and 35.0% average precision improvement while reducing memory consumption by at least 8.4% compared with the classic one-stage SSD, YOLO V3 and two-stage Faster R-CNN methods.

Deep learning has become widely used in image analysis. Transfer learning can make use of information from other datasets for the analysis of the chosen dataset. When there is a small number of images at hand, transfer learning using pre-trained models with coefficients already estimated from other datasets is recommended. This is in contrast to deep learning, where most model parameters are re-estimated. Deep transfer learning uses pre-trained models with fixed weight parameters in the lower layers; thus, deep learning can be viewed as a two-stage approach: (1) feature extraction from a lower neural network layer and (2) estimation of a neural network using the extracted features as inputs. Since deep transfer learning is feature extraction, we can extend the two-stage approach to a more general two-stage framework: (1) feature extraction using multiple methods and (2) machine learning methods taking extracted features as inputs. We evaluate the performance of methods with different Stage 1 and Stage 2 approaches in predicting the phenotype leaf numbers based on a multi-view plant imaging dataset. This paper contains an evaluation of different two-stage machine learning methods for multi-view image data in plant image phenotyping.

Breast cancer is the most prevalent cancer globally and presents a significant threat to human health. The low survival rate for advanced stages highlights the importance of early and accurate diagnosis. Pathology images, providing insights into cancer's cellular aggressiveness, are crucial for breast cancer diagnosis. Advances in deep learning have transformed automated medical image diagnosis. However, traditional deep learning methods, reliant on large labeled datasets, face challenges with the typically small size of medical image datasets. To overcome this, our study combines deep transfer learning (DTL) with convolutional neural networks(CNN). We utilize and enhance a ResNet50 model pretrained on a large dataset. Additionally, we apply four different deep transfer learning methods to feature extraction and classification of the BreakHis dataset, aiming to determine if breast cancer is benign or malignant. Ultimately, our system achieved the best average accuracy rates under four different magnifications: 98.57%, 96.83%, 97.76%, and 97.16%. These results demonstrate the accuracy and potential of deep transfer learning in classifying breast cancer histopathological images and its promising application in tumor prediction.

Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey

The large amounts of astrophysical data being provided by existing and future instrumentation require efficient and fast analysis tools. Transfer learning is a new technique promising higher accuracy in the derived data products, with information from one domain being transferred to improve the accuracy of a neural network model in another domain. In this work, we demonstrate the feasibility of applying the deep transfer learning (DTL) approach to high-resolution spectra in the framework of photospheric stellar parameter determination. To this end, we used 14 stars of the CARMENES survey sample with interferometric angular diameters to calculate the effective temperature, as well as six M dwarfs that are common proper motion companions to FGK-type primaries with known metallicity. After training a deep learning (DL) neural network model on synthetic PHOENIX-ACES spectra, we used the internal feature representations together with those 14+6 stars with independent parameter measurements as a new input for the transfer process. We compare the derived stellar parameters of a small sample of M dwarfs kept out of the training phase with results from other methods in the literature. Assuming that temperatures from bolometric luminosities and interferometric radii and metallicities from FGK+M binaries are sufficiently accurate, DTL provides a higher accuracy than our previous state-of-the-art DL method (mean absolute differences improve by 20 K for temperature and 0.2 dex for metallicity from DL to DTL when compared with reference values from interferometry and FGK+M binaries). Furthermore, the machine learning (internal) precision of DTL also improves as uncertainties are five times smaller on average. These results indicate that DTL is a robust tool for obtaining M-dwarf stellar parameters comparable to those obtained from independent estimations for well-known stars.

There is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases. A total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases. The results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists. This study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

This study was carried out with the aim of detecting the defects on the surfaces of metallic products, which are frequently encountered in our daily lives and widely used in industry, with deep learning. Many metal products in the industry undergo different processes during the production phase. As a result of these processes, the detection of defects such as breakage, cracking, etc. on the surfaces of metal products is carried out by quality control personnel or production personnel. However, this error detection made by manpower both slows down production and causes overlooked errors. In order to easily detect these defects, in our study, we used a dataset consisting of 285 images in total with images of two different surface defects and images containing flawless metal parts and a CNN architecture, ResNet50 architecture, to defects. There are three classes in total in the dataset. Two of these classes consist of images of different types of defects on the surface of the metal piston part used in air conditioners, and one of them consists of images of perfect metal piston parts. Convolutional Neural Network (CNN) method was used to determine the features of the images. Precision, recall, F1-Score and accuracy metrics were used to measure the performance of the model. With the ResNet50 architecture, defects on the surfaces of metal piston parts are detected quickly and with high accuracy. As a result of the study, it was suggested that the proposed model can detect surface defects that occur in the usage areas of metal products in various sectors more quickly and accurately using deep learning. This shows that the quality control problems experienced in the industry can be reduced by using deep learning, saving time and manpower.

Introduction: The early automatic diagnosis of the novel coronavirus (COVID-19) disease could be very helpful to reduce its spread around the world. In this study, we revisit the identification of COVID-19 from chest X-ray images using deep learning. Methods: We collected a relatively large COVID-19 dataset—compared with previous studies—containing 309 real COVID-19 chest X-ray images. We also prepared 2,000 chest X-ray images of pneumonia cases and 1,000 images of healthy controls. Deep transfer learning was used to detect abnormalities in our image dataset. We fine-tuned three, pre-trained convolutional neural network (CNN) models on a training dataset: DenseNet 121, NASNetLarge, and NASNet-Mobile. Results: The evaluation of our models on a test dataset showed that these models achieved an average sensitivity rate of approximately 99.45% and an average specificity rate of approximately 99.5%. Conclusion: A larger dataset of COVID-19 X-ray images could lead to more accurate and reliable identification of COVID-19 infections using deep transfer learning. However, the clinical diagnosis of COVID-19 disease is always necessary.