Deep Learning-Based Classification of Weld Surface Defects

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

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Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning

Applied Sciences ◽

10.3390/app10010087 ◽

2019 ◽

Vol 10 (1) ◽

pp. 87 ◽

Cited By ~ 5

Author(s):

Qingsheng Jiang ◽

Dapeng Tan ◽

Yanbiao Li ◽

Shiming Ji ◽

Chaopeng Cai ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Detection ◽

Convolutional Neural Network ◽

Surface Defects ◽

Engineering Application ◽

Light Surface ◽

Low Efficiency ◽

Shaft Surface

Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection.

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Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

International Journal of Software Innovation ◽

10.4018/ijsi.293269 ◽

2022 ◽

Vol 10 (1) ◽

pp. 0-0

Keyword(s):

Neural Network ◽

Deep Learning ◽

Brain Tumor ◽

Convolutional Neural Network ◽

Binary Classification ◽

Deep Convolutional Neural Network ◽

Neural Network Models ◽

Neural Network Approach ◽

Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the ﬁelds of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

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Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

Engineering Research Express ◽

10.1088/2631-8695/ac4834 ◽

2022 ◽

Author(s):

Canyi Du ◽

Rui Zhong ◽

Yishen Zhuo ◽

Xinyu Zhang ◽

Feifei Yu ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Pattern Recognition Method ◽

Recognition Method ◽

Vibration Signals ◽

One Dimensional ◽

Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

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Deep Learning Classification of Biomedical Text using Convolutional Neural Network

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2019.0100867 ◽

2019 ◽

Vol 10 (8) ◽

Author(s):

Rozilawati Dollah ◽

Chew Yi ◽

Norhawaniah Zakaria ◽

Mohd Shahizan ◽

Abd Wahid

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Biomedical Text

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A Novel Deep Learning Approach of Convolutional Neural Network and Random Forest Classifier for Fine-grained Sentiment Classification

International Journal on Electrical Engineering and Informatics ◽

10.15676/ijeei.2021.13.2.13 ◽

2021 ◽

Vol 13 (2) ◽

pp. 465-476

Author(s):

Siji George C. G. ◽

◽

B Sumathi ◽

Keyword(s):

Neural Network ◽

Deep Learning ◽

Random Forest ◽

Convolutional Neural Network ◽

Random Forest Classifier ◽

Sentiment Classification ◽

Learning Approach ◽

Fine Grained

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Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input

Sensors ◽

10.3390/s20144017 ◽

2020 ◽

Vol 20 (14) ◽

pp. 4017 ◽

Cited By ~ 3

Author(s):

Davor Kolar ◽

Dragutin Lisjak ◽

Michał Pająk ◽

Danijel Pavković

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Input Matrix ◽

Accelerometer Signal ◽

Rotary Machinery ◽

Signal Input

Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions 6400 × 1 × 3 is used as input for convolutional neural network training. Since convolutional neural networks can recognize patterns across input matrix, it is expected that wide input matrix containing vibration data should yield good classification performance. Using convolutional neural networks (CNN) trained model, classification in one of the four classes can be performed. Additionally, number of kernels of CNN is optimized using grid search, as preliminary studies show that alternating number of kernels impacts classification results. This study accomplished the effective classification of different rotary machinery states using convolutional artificial neural network for classification of raw three axis accelerometer signal input.

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Deep Convolutional Neural Network for Mapping Smallholder Agriculture Using High Spatial Resolution Satellite Image

Sensors ◽

10.3390/s19102398 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2398 ◽

Cited By ~ 7

Author(s):

Bin Xie ◽

Hankui K. Zhang ◽

Jie Xue

Keyword(s):

Neural Network ◽

Random Forest ◽

Land Cover ◽

Convolutional Neural Network ◽

Satellite Images ◽

Spatial Information ◽

Deep Convolutional Neural Network ◽

Crop Types ◽

Pooling Strategy

In classification of satellite images acquired over smallholder agricultural landscape with complex spectral profiles of various crop types, exploring image spatial information is important. The deep convolutional neural network (CNN), originally designed for natural image recognition in the computer vision field, can automatically explore high level spatial information and thus is promising for such tasks. This study tried to evaluate different CNN structures for classification of four smallholder agricultural landscapes in Heilongjiang, China using pan-sharpened 2 m GaoFen-1 (meaning high resolution in Chinese) satellite images. CNN with three pooling strategies: without pooling, with max pooling and with average pooling, were evaluated and compared with random forest. Two different numbers (~70,000 and ~290,000) of CNN learnable parameters were examined for each pooling strategy. The training and testing samples were systematically sampled from reference land cover maps to ensure sample distribution proportional to the reference land cover occurrence and included 60,000–400,000 pixels to ensure effective training. Testing sample classification results in the four study areas showed that the best pooling strategy was the average pooling CNN and that the CNN significantly outperformed random forest (2.4–3.3% higher overall accuracy and 0.05–0.24 higher kappa coefficient). Visual examination of CNN classification maps showed that CNN can discriminate better the spectrally similar crop types by effectively exploring spatial information. CNN was still significantly outperformed random forest using training samples that were evenly distributed among classes. Furthermore, future research to improve CNN performance was discussed.

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Deep Learning-Based Classification of Korean Basal Cell Carcinoma Using Convolutional Neural Network

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2019.2560 ◽

2019 ◽

Vol 9 (1) ◽

pp. 195-201

Author(s):

Hyun Jung Chung ◽

Yoon Jae Kim ◽

Howon Song ◽

Sung Ku Ahn ◽

Hyunggun Kim ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Basal Cell Carcinoma ◽

Convolutional Neural Network ◽

Cell Carcinoma ◽

Basal Cell

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CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-19-0189.1 ◽

2020 ◽

Vol 37 (9) ◽

pp. 1661-1668

Author(s):

Min Wang ◽

Shudao Zhou ◽

Zhong Yang ◽

Zhanhua Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Recognition ◽

Learning Ability ◽

Visualization Method ◽

Classification Methods ◽

Recognition Method ◽

Cloud Classification ◽

Average Accuracy

AbstractConventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of “shallow learning.” As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification.

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