scholarly journals Scratch Detection in Cars Using Mask Region Convolution Neural Networks

2020 ◽  
Author(s):  
Vijay K ◽  
Vijayakumar R ◽  
Sivaranjani P ◽  
Logeshwari R
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Quality Control ◽  
Automobile Industry ◽  
Digital Imaging ◽  
High Dimensional ◽  
Image Processing Algorithm ◽  
Aesthetic Standard ◽  
Fully Connected

This task depends on quality control in the vehicle business. It centers on the imprint and harms in new cars before producing to the client. This project presents the development of a system of recognition of defects and cosmetic imperfections in cars. This application gives a quick and strong robotized results. It likewise gives framework acknowledgement of scratches. In the as of now existing framework, the way toward distinguishing the scratches in car is finished by our mankind. Using the input frames, sections of the vehicles are entered for training, the last Fully-connected layer is altered so that it only has two exit categories: Sections with scratches and without scratches. This project is mainly developed to minimize manpower and maximize automation on quality department in automobile industry. It is a computer vision project. It includes task such as acquiring, processing, analyzing and understanding digital images and extraction of high dimensional data. An image processing algorithm is used in order to manipulate an image to achieve an aesthetic standard and to provide a translation between the human visual system and digital imaging services.

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

A Fast Image Processing Algorithm for Quality Control of Woven Textiles

1998 ◽  
pp. 471-479 ◽  
Author(s):  
Ch. Daul ◽  
R. Rösch ◽  
B. Claus ◽  
J. Grotepaß ◽  
U. Knaak ◽  
...  
Keyword(s):  
Image Processing ◽  
Quality Control ◽  
Processing Algorithm ◽  
Image Processing Algorithm

scholarly journals Convergence Behavior of DNNs with Mutual-Information-Based Regularization

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 727 ◽  
Author(s):  
Hlynur Jónsson ◽  
Giovanni Cherubini ◽  
Evangelos Eleftheriou
Keyword(s):  
Neural Networks ◽  
Mutual Information ◽  
Low Complexity ◽  
High Dimensional ◽  
Test Accuracy ◽  
Compression Phase ◽  
Hidden Layer ◽  
Low Dimensional ◽  
Fully Connected ◽  
Fully Connected Networks

Information theory concepts are leveraged with the goal of better understanding and improving Deep Neural Networks (DNNs). The information plane of neural networks describes the behavior during training of the mutual information at various depths between input/output and hidden-layer variables. Previous analysis revealed that most of the training epochs are spent on compressing the input, in some networks where finiteness of the mutual information can be established. However, the estimation of mutual information is nontrivial for high-dimensional continuous random variables. Therefore, the computation of the mutual information for DNNs and its visualization on the information plane mostly focused on low-complexity fully connected networks. In fact, even the existence of the compression phase in complex DNNs has been questioned and viewed as an open problem. In this paper, we present the convergence of mutual information on the information plane for a high-dimensional VGG-16 Convolutional Neural Network (CNN) by resorting to Mutual Information Neural Estimation (MINE), thus confirming and extending the results obtained with low-dimensional fully connected networks. Furthermore, we demonstrate the benefits of regularizing a network, especially for a large number of training epochs, by adopting mutual information estimates as additional terms in the loss function characteristic of the network. Experimental results show that the regularization stabilizes the test accuracy and significantly reduces its variance.

scholarly journals Guided Networks for Few-Shot Image Segmentation and Fully Connected CRFs

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1508
Author(s):  
Kun Zhang ◽  
Yuanjie Zheng ◽  
Xiaobo Deng ◽  
Weikuan Jia ◽  
Jian Lian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Fusion ◽  
Object Segmentation ◽  
Conditional Random Field ◽  
High Dimensional ◽  
Learning Method ◽  
Deep Convolutional Neural Networks ◽  
Structured Output ◽  
Fully Connected ◽  
Task Representations

The goal of the few-shot learning method is to learn quickly from a low-data regime. Structured output tasks like segmentation are challenging for few-shot learning, due to their being high-dimensional and statistically dependent. For this problem, we propose improved guided networks and combine them with a fully connected conditional random field (CRF). The guided network extracts task representations from annotated support images through feature fusion to do fast, accurate inference on new unannotated query images. By bringing together few-shot learning methods and fully connected CRFs, our method can do accurate object segmentation by overcoming poor localization properties of deep convolutional neural networks and can quickly updating tasks, without further optimization, when faced with new data. Our guided network is at the forefront of accuracy for the terms of annotation volume and time.

scholarly journals Hand Detection for Safety to Avoid Accidents

2021 ◽  
Vol 9 (VI) ◽  
pp. 3547-3552
Author(s):  
Eega Krishna Chaithanya
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Object Detection ◽  
Time Data ◽  
Image Processing Algorithm ◽  
Hand Detection ◽  
Safety Level ◽  
Real Time Data ◽  
Imaginary Line ◽  
Stop Line

Detecting the hand when it crosses the safety level and in return it also raises an alert in the form of alarm. So that the threat can be identified and proper measures are taken to overcome that. The methodology of the project goes as follows, taking input from camera , Image processing to detect hand, Projecting a line using computer vision, Raising alarm when hand crosses this projected safety line. The real time data is taken from the camera as an input to the Image processing algorithm. Then this input is processed to find the hand in image in it and checks whether the hand is crossing that safety line. If that hand is crossing the safety line we can simply raise alarm. The applications of the project are to the Employees who are working at industry are pushing the material into shredder machine. But somehow while pushing these material into shredder machine the employees are pushing their hands itself in the flow of work and the hands of employees were cut in that cause. So from a certain distance from shredder machine input we project a imaginary line using computer vision, So that if any hand crossing that imaginary line which is for safety we will raise an alarm. In addition, we can also extend the applications, by just replacing hand with the Bike, we can detect the bike, which is crossing the staggered stop line, and we can punish or fine them. As a part of object detection we are using Single short multibox detector.

scholarly journals An Analysis on Object Recognition Using Convolutional Neural Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 1928-1935
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
Object Detection ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Critical Analysis ◽  
Data Volume ◽  
Fundamental Reason

The global development and progress in scientific paraphernalia and technology is the fundamental reason for the rapid increasein the data volume. Several significant techniques have been introducedfor image processing and object detection owing to this advancement. The promising features and transfer learning of ConvolutionalNeural Network (CNN) havegained much attention around the globe by researchers as well as computer vision society, as a result of which, several remarkable breakthroughs were achieved. This paper comprehensively reviews the data classification, history as well as architecture of CNN and well-known techniques bytheir boons and absurdities. Finally, a discussion for implementation of CNN over object detection for effectual results based on their critical analysis and performances is presented

scholarly journals Kayu7net: Identifikasi dan Evaluasi F-Measure Citra Kayu berbasis Deep Convolutional Neural Network (DCNN)

2020 ◽  
Vol 7 (6) ◽  
pp. 1089
Author(s):  
Iwan Muhammad Erwin ◽  
Risnandar Risnandar ◽  
Esa Prakarsa ◽  
Bambang Sugiarto
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Iteration Process ◽  
Batch Size ◽  
Wood Identification ◽  
Deep Convolutional Neural Networks ◽  
Fully Connected ◽  
F Measure

<p class="Abstrak">Identifikasi kayu salah satu kebutuhan untuk mendukung pemerintah dan kalangan bisnis kayu untuk melakukan perdagangan kayu secara legal. Keahlian khusus dan waktu yang cukup dibutuhkan untuk memproses identifikasi kayu di laboratorium. Beberapa metodologi penelitian sebelumnya, proses identifikasi kayu masih dengan cara menggabungkan sistem manual menggunakan anatomi DNA kayu. Sedangkan penggunaan sistem komputer diperoleh dari citra penampamg melintang kayu secara proses mikrokopis dan makroskopis. Saat ini, telah berkembang teknologi computer vision dan machine learning untuk mengidentifikasi berbagai jenis objek, salah satunya citra kayu. Penelitian ini berkontribusi dalam mengklasifikasi beberapa spesies kayu yang diperdagangkan menggunakan Deep Convolutional Neural Networks (DCNN). Kebaruan penelitian ini terletak pada arsitektur DCNN yang bernama Kayu7Net. Arsitektur Kayu7Net yang diusulkan memiliki tiga lapisan konvolusi terhadap tujuh spesies dataset citra kayu. Pengujian dengan merubah citra input menjadi berukuran 600×600, 300×300, dan 128×128 piksel serta masing-masing diulang pada epoch 50 dan 100. DCNN yang diusulkan menggunakan fungsi aktivasi ReLU dengan batch size 32. ReLU bersifat lebih konvergen dan cepat saat proses iterasi. Sedangkan Fully-Connected (FC) berjumlah 4 lapisan akan menghasilkan proses training yang lebih efisien. Hasil eksperimen memperlihatkan bahwa Kayu7Net yang diusulkan memiliki nilai akurasi sebesar 95,54%, precision sebesar 95,99%, recall sebesar 95,54%, specificity sebesar 99,26% dan terakhir, nilai F-measure sebesar 95,46%. Hasil ini menunjukkan bahwa arsitektur Kayu7Net lebih unggul sebesar 1,49% pada akurasi, 2,49% pada precision, dan 5,26% pada specificity dibandingkan penelitian sebelumnya.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em>Wood identification is one of the needs to support the government and the wood business community for a legally wood trading system. Special expertise and sufficient time are needed to process wood identification in the laboratory. Some previous research works show that the process of identifying wood combines a manual system using a wood DNA anatomy. While, the use of a computer system is obtained from the wood image of microscopic and macroscopic process. Recently, the latest technology has developed by using the machine learning and computer vision to identify many objects, the one of them is wood image. This research contributes to classify several the traded wood species by using Deep Convolutional Neural Networks (DCNN). The novelty of this research is in the DCNN architecture, namely Kayu7Net. The proposed of Kayu7Net Architecture has three convolution layers of the seven species wood image dataset. The testing changes the wood image input to 600×600, 300×300, and 128×128 pixel, respectively, and each of them repeated until 50 and 100 epoches, respectively. The proposed DCNN uses the ReLU activation function and batch size 32. The ReLU is more convergent and faster during the iteration process. Whereas, the 4 layers of Fully-Connected (FC) will produce a more efficient training process. The experimental results show that the proposed Kayu7Net has an accuracy value of 95.54%, a precision of 95.99%, a recall of 95.54%, a specificity of 99.26% and finally, an F-measure value of 95.46%. These results indicate that Kayu7Net is superior by 1.49% of accuracy, 2.49% of precision, and 5.26% of specificity compared to the previous work. </em></p><p class="Abstrak"> </p>

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