INSPECTION OF DEFECTIVE PRODUCTS IN THE INDUSTRY WITH IMAGE PROCESSING BASED ON DEEP LEARNING METHODS

2021 ◽  
Vol 0 (15) ◽  
pp. 0-0
Author(s):  
Tuğba Özge ONUR
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Control Systems ◽  
Learning Model ◽  
Submersible Pump ◽  
Learning Methods ◽  
Pump Impeller ◽  
Maximum Accuracy ◽  
Testing Stage ◽  
Deep Learning Model

Aim: This paper studies to detect the undetectable defects in casting such as air holes, pinholes, burrs, tensile defects, mold material defects, metal casting defects, metallurgical defects, and etc. Through images via deep learning methods. Method: In the study, an automatic reading review for submersible pump impeller is proposed and a deep learning model is developed. The data set of the images include defective and smooth submersible pump impeller has been utilized to prove the performance of the designed network architecture. Results: According to the obtained results, maximum accuracy of 89% for the classifier has been achieved in the training stage and it reached a maximum accuracy of 93% in the testing stage. Conclusion: Along with the developing systems, quality control systems have been evolved through image processing. Thanks to the image processing-based control systems, the capacity and efficiency of the production facilities can be increased and perfect products can be delivered to the end-user by making precise measurements. In this study, since casting is an important and frequently used process in the industry, its defects are evaluated and a deep learning model to automate the review process and detect the defective products of submersible pump impellers is presented. The success rate of 89% obtained show that the defective product inspection in the industry can be performed over the images by using a convolutional neural network (CNN) architecture.

scholarly journals A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 39
Author(s):  
Zhiyuan Xie ◽  
Shichang Du ◽  
Jun Lv ◽  
Yafei Deng ◽  
Shiyao Jia
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Model ◽  
Recurrent Network ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Second Phase ◽  
Learning Methods ◽  
Useful Life ◽  
Deep Learning Model

Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.

scholarly journals Deep Learning Based Ethiopian Car’s License Plate Detection and Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 5730-5737
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Learning Model ◽  
Motor Vehicles ◽  
License Plate ◽  
License Plate Recognition ◽  
Computer Algorithms ◽  
Wide Range ◽  
Processing Techniques ◽  
Deep Learning Model

Digital Image Processing is application of computer algorithms to process, manipulate and interpret images. As a field it is playing an increasingly important role in many aspects of people’s daily life. Even though Image Processing has accomplished a great deal on its own, nowadays researches are being conducted in using it with Deep Learning (which is part of a broader family, Machine Learning) to achieve better performance in detecting and classifying objects in an image. Car’s License Plate Recognition is one of the hottest research topics in the domain of Image Processing (Computer Vision). It is having wide range of applications since license number is the primary and mandatory identifier of motor vehicles. When it comes to license plates in Ethiopia, they have unique features like Amharic characters, differing dimensions and plate formats. Although there is a research conducted on ELPR, it was attempted using the conventional image processing techniques but never with deep learning. In this proposed research an attempt is going to be made in tackling the problem of ELPR with deep learning and image processing. Tensorflow is going to be used in building the deep learning model and all the image processing is going to be done with OpenCV-Python. So, at the end of this research a deep learning model that recognizes Ethiopian license plates with better accuracy is going to be built.

scholarly journals DiPLIP: Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1664
Author(s):  
Yoon-Ki Kim ◽  
Yongsung Kim
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Parallel Processing ◽  
Real Time ◽  
Large Scale ◽  
Learning Model ◽  
Streaming Data ◽  
Model Inference ◽  
Processing Platform ◽  
Deep Learning Model

Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the distributed parallel processing system, the orchestration of technology has become crucial for proper management of computing resources, in terms of allocating computing resources, setting up a programming environment, and deploying user applications. In this paper, we present a new distributed parallel processing platform for real-time large-scale image processing based on deep learning model inference, called DiPLIP. It provides a scheme for large-scale real-time image inference using buffer layer and a scalable parallel processing environment according to the size of the stream image. It allows users to easily process trained deep learning models for processing real-time images in a distributed parallel processing environment at high speeds, through the distribution of the virtual machine container.

scholarly journals Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2012
Author(s):  
Jiameng Gao ◽  
Chengzhong Liu ◽  
Junying Han ◽  
Qinglin Lu ◽  
Hengxing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Wheat Variety ◽  
Learning Model ◽  
Wheat Cultivars ◽  
Learning Methods ◽  
Wheat Varieties ◽  
Deep Learning Model

Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production.

Detection of solder paste defects with an optimization‐based deep learning model using image processing techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ali Sezer ◽  
Aytaç Altan
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Learning Model ◽  
Production Costs ◽  
Solder Paste ◽  
Data Set ◽  
Content Type ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Deep Learning Model

Purpose In the production processes of electronic devices, production activities are interrupted due to the problems caused by soldering defects during the assembly of surface-mounted elements on printed circuit boards (PCBs), and this leads to an increase in production costs. In solder paste applications, defects that may occur in electronic cards are usually noticed at the last stage of the production process. This situation reduces the efficiency of production and causes delays in the delivery schedule of critical systems. This study aims to overcome these problems, optimization based deep learning model has been proposed by using 2D signal processing methods. Design/methodology/approach An optimization-based deep learning model is proposed by using image-processing techniques to detect solder paste defects on PCBs with high performance at an early stage. Convolutional neural network, one of the deep learning methods, is trained using the data set obtained for this study, and pad regions on PCB are classified. Findings A total of six types of classes used in the study consist of uncorrectable soldering, missing soldering, excess soldering, short circuit, undefined object and correct soldering, which are frequently used in the literature. The validity of the model has been tested on the data set consisting of 648 test data. Originality/value The effect of image processing and optimization methods on model performance is examined. With the help of the proposed model, defective solder paste areas on PCBs are detected, and these regions are visualized by taking them into a frame.

scholarly journals Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics

2021 ◽  
Vol 13 (7) ◽  
pp. 1360
Author(s):  
A-Xing Zhu ◽  
Fang-He Zhao ◽  
Hao-Bo Pan ◽  
Jun-Zhi Liu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Rice Paddy ◽  
Learning Model ◽  
Field Sampling ◽  
Learning Methods ◽  
Training Samples ◽  
Field Samples ◽  
Deep Learning Model

Two main approaches are used in mapping rice paddy distribution from remote sensing images: phenological methods or machine learning methods. The phenological methods can map rice paddy distribution in a simple way but with limited accuracy. Machine learning, particularly deep learning, methods that learn the spectral signatures can achieve higher accuracy yet require a large number of field samples. This paper proposed a pheno-deep method to couple the simplicity of the phenological methods and the learning ability of the deep learning methods for mapping rice paddy at high accuracy without the need of field samples. The phenological method was first used to initially delineate the rice paddy for the purpose of creating training samples. These samples were then used to train the deep learning model. The trained deep learning model was applied to map the spatial distribution of rice paddy. The effectiveness of the pheno-deep method was evaluated in Jin’an District, Lu’an City, Anhui Province, China. Results show that the pheno-deep method achieved a high performance with the overall accuracy, the precision, the recall, and AUC (area under curve) being 88.8%, 87.2%, 91.1%, and 94.4%, respectively. The pheno-deep method achieved a much better performance than the phenological alone method and can overcome the noises in the training samples from the phenological method. The overall accuracy of the pheno-deep method is only 2.4% lower than that of the deep learning alone method trained with field samples and this difference is not statistically significant. In addition, the pheno-deep method requires no field sampling, which would be a noteworthy advantage for situations when large training samples are difficult to obtain. This study shows that by combining knowledge-based methods with data-driven methods, it is possible to achieve high mapping accuracy of geographic variables using remote sensing even with little field sampling efforts.

scholarly journals Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Huan Yang ◽  
Lili Chen ◽  
Zhiqiang Cheng ◽  
Minglei Yang ◽  
Jianbo Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Intraclass Correlation ◽  
Learning Model ◽  
Normal Lung ◽  
Histopathological Analysis ◽  
Learning Methods ◽  
Cell Lung Carcinoma ◽  
Deep Learning Model ◽  
Whole Slide Images

Abstract Background Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs. Methods We initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People’s Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model. Results We developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. Conclusions Multi-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios.

scholarly journals Intelligent Data Analytics for Diagnosing Melanoma Skin Lesions via Deep Learning in IoT System

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shixiang Zhang ◽  
Shuaiqi Huang ◽  
Hongkai Wu ◽  
Zicong Yang ◽  
Yinda Chen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Histopathological Examination ◽  
Diagnostic System ◽  
Skin Lesions ◽  
Learning Model ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Deep Learning Model

Melanoma is considered to be one of the most dangerous human malignancy, which is diagnosed visually or by dermoscopic analysis and histopathological examination. However, as these traditional methods are based on human experience and implemented manually, there have been great limitations for general usability in current clinical practice. In this paper, a novel hybrid machine learning approach is proposed to identify melanoma for skin healthcare in various cases. The proposed approach consists of classic machine learning methods, including convolutional neural networks (CNNs), EfficientNet, and XGBoost supervised machine learning. In the proposed approach, a deep learning model is trained directly from raw pixels and image labels for classification of skin lesions. Then, solely based on modeling of various features from patients, an XGBoost model is adopted to predict skin cancer. Following that, a diagnostic system which composed of the deep learning model and XGBoost model is developed to further improve the prediction efficiency and accuracy. Different from experience-based methods and solely image-based machine learning methods, the proposed approach is developed based on the theory of deep learning and feature engineering. Experiments show that the hybrid model outperforms single model like the traditional deep learning model or XGBoost model. Moreover, the data-driven-based characteristics can help the proposed approach develop a guideline for image analysis in other medical applications.

scholarly journals Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kai-Yao Huang ◽  
Justin Bo-Kai Hsu ◽  
Tzong-Yi Lee
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Amino Acid ◽  
Learning Model ◽  
Training Dataset ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Lysine Succinylation ◽  
Testing Dataset ◽  
Deep Learning Model

Abstract Succinylation is a type of protein post-translational modification (PTM), which can play important roles in a variety of cellular processes. Due to an increasing number of site-specific succinylated peptides obtained from high-throughput mass spectrometry (MS), various tools have been developed for computationally identifying succinylated sites on proteins. However, most of these tools predict succinylation sites based on traditional machine learning methods. Hence, this work aimed to carry out the succinylation site prediction based on a deep learning model. The abundance of MS-verified succinylated peptides enabled the investigation of substrate site specificity of succinylation sites through sequence-based attributes, such as position-specific amino acid composition, the composition of k-spaced amino acid pairs (CKSAAP), and position-specific scoring matrix (PSSM). Additionally, the maximal dependence decomposition (MDD) was adopted to detect the substrate signatures of lysine succinylation sites by dividing all succinylated sequences into several groups with conserved substrate motifs. According to the results of ten-fold cross-validation, the deep learning model trained using PSSM and informative CKSAAP attributes can reach the best predictive performance and also perform better than traditional machine-learning methods. Moreover, an independent testing dataset that truly did not exist in the training dataset was used to compare the proposed method with six existing prediction tools. The testing dataset comprised of 218 positive and 2621 negative instances, and the proposed model could yield a promising performance with 84.40% sensitivity, 86.99% specificity, 86.79% accuracy, and an MCC value of 0.489. Finally, the proposed method has been implemented as a web-based prediction tool (CNN-SuccSite), which is now freely accessible at http://csb.cse.yzu.edu.tw/CNN-SuccSite/.

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