• Title/Summary/Keyword: CNN algorithm

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Estimation of gender and age using CNN-based face recognition algorithm

  • Lim, Sooyeon
    • International journal of advanced smart convergence
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    • v.9 no.2
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    • pp.203-211
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    • 2020
  • This study proposes a method for estimating gender and age that is robust to various external environment changes by applying deep learning-based learning. To improve the accuracy of the proposed algorithm, an improved CNN network structure and learning method are described, and the performance of the algorithm is also evaluated. In this study, in order to improve the learning method based on CNN composed of 6 layers of hidden layers, a network using GoogLeNet's inception module was constructed. As a result of the experiment, the age estimation accuracy of 5,328 images for the performance test of the age estimation method is about 85%, and the gender estimation accuracy is about 98%. It is expected that real-time age recognition will be possible beyond feature extraction of face images if studies on the construction of a larger data set, pre-processing methods, and various network structures and activation functions have been made to classify the age classes that are further subdivided according to age.

Railway sleeper crack recognition based on edge detection and CNN

  • Wang, Gang;Xiang, Jiawei
    • Smart Structures and Systems
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    • v.28 no.6
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    • pp.779-789
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    • 2021
  • Cracks in railway sleeper are an inevitable condition and has a significant influence on the safety of railway system. Although the technology of railway sleeper condition monitoring using machine learning (ML) models has been widely applied, the crack recognition accuracy is still in need of improvement. In this paper, a two-stage method using edge detection and convolutional neural network (CNN) is proposed to reduce the burden of computing for detecting cracks in railway sleepers with high accuracy. In the first stage, the edge detection is carried out by using the 3×3 neighborhood range algorithm to find out the possible crack areas, and a series of mathematical morphology operations are further used to eliminate the influence of noise targets to the edge detection results. In the second stage, a CNN model is employed to classify the results of edge detection. Through the analysis of abundant images of sleepers with cracks, it is proved that the cracks detected by the neighborhood range algorithm are superior to those detected by Sobel and Canny algorithms, which can be classified by proposed CNN model with high accuracy.

Image Classification using Deep Learning Algorithm and 2D Lidar Sensor (딥러닝 알고리즘과 2D Lidar 센서를 이용한 이미지 분류)

  • Lee, Junho;Chang, Hyuk-Jun
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1302-1308
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    • 2019
  • This paper presents an approach for classifying image made by acquired position data from a 2D Lidar sensor with a convolutional neural network (CNN). Lidar sensor has been widely used for unmanned devices owing to advantages in term of data accuracy, robustness against geometry distortion and light variations. A CNN algorithm consists of one or more convolutional and pooling layers and has shown a satisfactory performance for image classification. In this paper, different types of CNN architectures based on training methods, Gradient Descent(GD) and Levenberg-arquardt(LM), are implemented. The LM method has two types based on the frequency of approximating Hessian matrix, one of the factors to update training parameters. Simulation results of the LM algorithms show better classification performance of the image data than that of the GD algorithm. In addition, the LM algorithm with more frequent Hessian matrix approximation shows a smaller error than the other type of LM algorithm.

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm

  • Lee, Jae-Hong;Kim, Do-hyung;Jeong, Seong-Nyum;Choi, Seong-Ho
    • Journal of Periodontal and Implant Science
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    • v.48 no.2
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    • pp.114-123
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    • 2018
  • Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

Video Stabilization Algorithm of Shaking image using Deep Learning (딥러닝을 활용한 흔들림 영상 안정화 알고리즘)

  • Lee, Kyung Min;Lin, Chi Ho
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.19 no.1
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    • pp.145-152
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    • 2019
  • In this paper, we proposed a shaking image stabilization algorithm using deep learning. The proposed algorithm utilizes deep learning, unlike some 2D, 2.5D and 3D based stabilization techniques. The proposed algorithm is an algorithm that extracts and compares features of shaky images through CNN network structure and LSTM network structure, and transforms images in reverse order of movement size and direction of feature points through the difference of feature point between previous frame and current frame. The algorithm for stabilizing the shake is implemented by using CNN network and LSTM structure using Tensorflow for feature extraction and comparison of each frame. Image stabilization is implemented by using OpenCV open source. Experimental results show that the proposed algorithm can be used to stabilize the camera shake stability in the up, down, left, and right shaking images.

Semiconductor Process Inspection Using Mask R-CNN (Mask R-CNN을 활용한 반도체 공정 검사)

  • Han, Jung Hee;Hong, Sung Soo
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.3
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    • pp.12-18
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    • 2020
  • In semiconductor manufacturing, defect detection is critical to maintain high yield. Currently, computer vision systems used in semiconductor photo lithography still have adopt to digital image processing algorithm, which often occur inspection faults due to sensitivity to external environment. Thus, we intend to handle this problem by means of using Mask R-CNN instead of digital image processing algorithm. Additionally, Mask R-CNN can be trained with image dataset pre-processed by means of the specific designed digital image filter to extract the enhanced feature map of Convolutional Neural Network (CNN). Our approach converged advantage of digital image processing and instance segmentation with deep learning yields more efficient semiconductor photo lithography inspection system than conventional system.

Comparison of CNN and YOLO for Object Detection (객체 검출을 위한 CNN과 YOLO 성능 비교 실험)

  • Lee, Yong-Hwan;Kim, Youngseop
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.1
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    • pp.85-92
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    • 2020
  • Object detection plays a critical role in the field of computer vision, and various researches have rapidly increased along with applying convolutional neural network and its modified structures since 2012. There are representative object detection algorithms, which are convolutional neural networks and YOLO. This paper presents two representative algorithm series, based on CNN and YOLO which solves the problem of CNN bounding box. We compare the performance of algorithm series in terms of accuracy, speed and cost. Compared with the latest advanced solution, YOLO v3 achieves a good trade-off between speed and accuracy.

Concrete Crack Detection and Visualization Method Using CNN Model (CNN 모델을 활용한 콘크리트 균열 검출 및 시각화 방법)

  • Choi, Ju-hee;Kim, Young-Kwan;Lee, Han-Seung
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2022.04a
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    • pp.73-74
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    • 2022
  • Concrete structures occupy the largest proportion of modern infrastructure, and concrete structures often have cracking problems. Existing concrete crack diagnosis methods have limitations in crack evaluation because they rely on expert visual inspection. Therefore, in this study, we design a deep learning model that detects, visualizes, and outputs cracks on the surface of RC structures based on image data by using a CNN (Convolution Neural Networks) model that can process two- and three-dimensional data such as video and image data. do. An experimental study was conducted on an algorithm to automatically detect concrete cracks and visualize them using a CNN model. For the three deep learning models used for algorithm learning in this study, the concrete crack prediction accuracy satisfies 90%, and in particular, the 'InceptionV3'-based CNN model showed the highest accuracy. In the case of the crack detection visualization model, it showed high crack detection prediction accuracy of more than 95% on average for data with crack width of 0.2 mm or more.

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Implementation and Verification of Multi-level Convolutional Neural Network Algorithm for Identifying Unauthorized Image Files in the Military (국방분야 비인가 이미지 파일 탐지를 위한 다중 레벨 컨볼루션 신경망 알고리즘의 구현 및 검증)

  • Kim, Youngsoo
    • Journal of Korea Multimedia Society
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    • v.21 no.8
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    • pp.858-863
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    • 2018
  • In this paper, we propose and implement a multi-level convolutional neural network (CNN) algorithm to identify the sexually explicit and lewdness of various image files, and verify its effectiveness by using unauthorized image files generated in the actual military. The proposed algorithm increases the accuracy by applying the convolutional artificial neural network step by step to minimize classification error between similar categories. Experimental data have categorized 20,005 images in the real field into 6 authorization categories and 11 non-authorization categories. Experimental results show that the overall detection rate is 99.51% for the image files. In particular, the excellence of the proposed algorithm is verified through reducing the identification error rate between similar categories by 64.87% compared with the general CNN algorithm.

Study on the Surface Defect Classification of Al 6061 Extruded Material By Using CNN-Based Algorithms (CNN을 이용한 Al 6061 압출재의 표면 결함 분류 연구)

  • Kim, S.B.;Lee, K.A.
    • Transactions of Materials Processing
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    • v.31 no.4
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    • pp.229-239
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    • 2022
  • Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.