scholarly journals Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric

2021 ◽  
Vol 1 (2) ◽  
pp. 9-13
Author(s):  
Subrata Das ◽  
Sundaramurthy S ◽  
Aiswarya M ◽  
Suresh Jayaram
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Textile Industry ◽  
Woven Fabric ◽  
Defect Identification ◽  
Complete Inspection ◽  
Hidden Layer ◽  
Deep Learning Neural Network

Inspection is the most important role in textile industry which declares the quality of the apparel product. Many Industries were improving their production or quality using Artificial Intelligence. Inspection of fabric in textile industry takes more time and labours. In order to reduce the number of labours and time taken to complete inspection, computerized image processing is done to identify the defects. It gives the accurate result in less time, thereby saves time and increases the production. The convolutional neural network in deep learning is mainly used for image processing for defect detection and classification. The high quality images are given as input, and then the images were used to train the deep learning neural network. Thewoven fabricdefects such as Holes, Selvedge tails, Stains, Wrong drawing and Snarlswere identified by using Convolutional Neural Network. The sample images were collected from the SkyCotex India Pvt.Ltd. The sample images were processed in CNN based machine learning ingoogle platform; the network has a input layer, n number of hidden layer and output layer. The neural network is trained and tested with the samples and the result obtained is used to calculate the efficiency of defect identification.

scholarly journals Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric

2021 ◽  
pp. 9-13
Author(s):  
Subrata Das ◽  
◽  
Sundaramurthy S ◽  
Aiswarya M ◽  
Suresh Jayaram ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Textile Industry ◽  
Woven Fabric ◽  
Defect Identification ◽  
Complete Inspection ◽  
Hidden Layer ◽  
Deep Learning Neural Network

Inspection is the most important role in textile industry which declares the quality of the apparel product. Many Industries were improving their production or quality using Artificial Intelligence. Inspection of fabric in textile industry takes more time and labours. In order to reduce the number of labours and time taken to complete inspection, computerized image processing is done to identify the defects. It gives the accurate result in less time, thereby saves time and increases the production. The convolutional neural network in deep learning is mainly used for image processing for defect detection and classification. The high quality images are given as input, and then the images were used to train the deep learning neural network. The woven fabric defects such as Holes, Selvedge tails, Stains, Wrong drawing and Snarls were identified by using Convolutional Neural Network. The sample images were collected from the Sky Cotex India Pvt. Ltd. The sample images were processed in CNN based machine learning in google platform; the network has a input layer, n number of hidden layer and output layer. The neural network is trained and tested with the samples and the result obtained is used to calculate the efficiency of defect identification.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

IMPLEMENTASI DEEP LEARNING MENGGUNAKAN FRAMEWORK TENSORFLOW DENGAN METODE FASTER REGIONAL CONVOLUTIONAL NEURAL NETWORK UNTUK PENDETEKSIAN JERAWAT

2018 ◽  
Vol 23 (2) ◽  
pp. 89-102
Author(s):  
Yunita Aulia Hasma ◽  
Widya Silfianti
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network

Jerawat sering dialami oleh kaum wanita maupun pria dari usia remaja hingga dewasa. Banyak rumah sakit dan klinik kecantikan yang dapat di datangi oleh para penderita untuk memeriksakan jerawat tersebut. Penelitian ini merupakan implementasi dari pendeteksian jerawat menggunakan image processing dan secara realtime, lalu sistem akan mengklasifikasikan jerawat yang ada pada wajah. Jerawat yang dapat dikenali oleh sistem ini yaitu jerawat, bekas, dan pus. Sistem deteksi dan klasifikasi ini dibuat dengan metode deep learning dengan menggunakan bahasa pemrograman Python, yang dibantu dengan menggunakan framework TensorFlow dengan model Faster R-CNN. Sistem ini hanya dapat berjalan di laptop dengan memiliki Python versi 3.6 di dalamnya dan telah memliki library Numpy, TkInter, Matplotlib, dan OpenCV dan juga memiliki kamera pada laptop yang digunakan agar dapat menjalankan sistem secara realtime yang didukung dengan GPU yang memadai. Perancangan alur aplikasi menggunakan flowchart diagram. Hasil uji terhadap sistem menggunakan perbandingan objek yang terdeteksi dengan yang seharusnya lalu dibagi dan dikalikan dengan seratus persen. Hasil yang didapat dari pengujian cukup baik menggunakan metode deep learning.

scholarly journals Image Processing Failure and Deep Learning Success in Lawn Measurement

2020 ◽  
Author(s):  
J. Wilkins Wilkins ◽  
M. V. Nguyen Nguyen ◽  
B. Rahmani Rahmani
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Area Measurement ◽  
Learning Methods ◽  
Hierarchical Networks ◽  
Processing Methods ◽  
Learning Success ◽  
Segmented Images

Lawn area measurement is an application of image processing and deep learning. Researchers used hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’ effectiveness and accuracy varies. In this project, image processing and deep learning methods were used to find the best way to measure the lawn area. Three image processing methods using OpenCV compared to convolutional neural network, which is one of the most famous, and effective deep learning methods. We used Keras and TensorFlow to estimate the lawn area. Convolutional neural network or shortly CNN shows very high accuracy (94-97%). In image processing methods, thresholding with 80-87% accuracy and edge detection are the most effective methods to measure the lawn area while the method ofcontouring with 26-31% accuracy does not calculate the lawn area successfully. We may conclude that deep learning methods, especially CNN, could be the best detective method comparing to image processing learning techniques.

scholarly journals Fusion of Multi Modal Lumber Spine Scans Using Wavelet and Convolutional Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1704-1709
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Single Image ◽  
Mr Images ◽  
Planning Time ◽  
Medical Practitioners ◽  
Fused Image

Multiple medical images of different modalities are fused together to generate a new more informative image thereby reducing the treatment planning time of medical practitioners. In recent years, wavelets and deep learning methods have been widely used in various image processing applications. In this study, we present convolutional neural network and wavelet based fusion of MR and CT images of lumber spine to generate a single image which comprises all the important features of MR and CT images. Both CT and MR images are first decomposed into detail and approximate coefficients using wavelets. Then the corresponding detail and approximate coefficients are fused using convolutional neural network framework. Inverse wavelet transform is then used to generate fused image. The experimental results indicate that the proposed approach achieves good performance as compared to conventional methods

scholarly journals Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Ge Ren ◽  
Sai-kit Lam ◽  
Jiang Zhang ◽  
Haonan Xiao ◽  
Andy Lai-yin Cheung ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Radiation Therapy ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Contrast Enhancement ◽  
Lung Tissue ◽  
Ct Images ◽  
Normal Lung ◽  
Ct Contrast

Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3–5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy.

scholarly journals Differentiating Birds and Animals using Deep Learning Neural Network with Image Processing Approach

2020 ◽  
Vol 5 (9) ◽  
pp. 609-613
Author(s):  
K Pandiaraj ◽  
P Sivakumar ◽  
V Nandhini ◽  
S Parkav
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Real Time ◽  
The Real ◽  
Processing Approach ◽  
Human Effort ◽  
Deep Learning Neural Network

In farms we can see that the birds and animals destroying the crops. The movement of birds and animals cannot be controlled by any method. We can only drive away them. To drive away them, humans are used. To reduce the human effort we have introduced a method using image processing. In this method, the real time images are given as input and sound will be derived as output. The image given as input is compared with the trained images and classified into birds and animals. After the identification, birds can be driven away by using cracker sound and animals can be driven away by using a human sound.

scholarly journals Application of deep learning for division of petroleum reservoirs

2018 ◽  
Vol 246 ◽  
pp. 03004
Author(s):  
Yaqiong Qin ◽  
Zhaohui Ye ◽  
Conghui Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Bp Neural Network ◽  
Network Models ◽  
Petroleum Reservoirs ◽  
Division Problem ◽  
Learning Models ◽  
Neural Network Models ◽  
Hidden Layer

Traditional methods of dividing petroleum reservoirs are inefficient, and the accuracy of onehidden-layer BP neural network is not ideal when applied to dividing reservoirs. This paper proposes to use the deep learning models to solve the reservoir division problem. We apply multiple-hidden-layer BP neural network and convolutional neural network models, and adjust the network structures according to the characteristics of the reservoir problem. The results show that the deep learning models are better than onehidden- layer BP neural network, and the performance of the convolutional neural network is very close to the artificial work.

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