scholarly journals Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

2021 ◽  
Vol 11 (15) ◽  
pp. 6721
Author(s):  
Jinyeong Wang ◽  
Sanghwan Lee
Keyword(s):  
Neural Networks ◽  
Machine Vision ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Image Data ◽  
Manufacturing Productivity ◽  
Smart Factories ◽  
Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

scholarly journals Assessing Wear Out of Tyre using Opencv & Convolutional Neural Networks

2021 ◽  
Vol 2089 (1) ◽  
pp. 012001
Author(s):  
C Kishor Kumar Reddy ◽  
P R Anisha ◽  
R Madana Mohana
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Wear Testing ◽  
Timely Manner ◽  
Safe Driving ◽  
The Road ◽  
Tire Wear ◽  
Wear And Tear

Abstract This work proposes a process to detect the wear and tear of car tires. Tire is the only part of the road that does not interact with the road. The condition of the wheel should therefore be monitored in a timely manner for safe driving. Tired fatigue occurs due to limitations such as that the tread limit is less than 1.6 cm, the damage to the rubber, where there are pipes around 4 to 5, the affected tire. We look at some of the above limitations of tire wear testing using computer viewing techniques such as opencv and convolutional neural networks. Opencv and convolutional neural networks are widely used for object detection and image classification. We used these methods and obtained 90.90% accuracy, with which we can predict tire wear to avoid dangerous accidents..

scholarly journals Real Time Object Detection using CNN

2018 ◽  
Vol 7 (2.24) ◽  
pp. 33
Author(s):  
Akash Tripathi ◽  
T V. Ajay Kumar ◽  
Tarun Kanth Dhansetty ◽  
J Selva Kumar
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Image Classification ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Small Sample ◽  
Small Data ◽  
Processing Unit ◽  
Sift Algorithm ◽  
Central Processing

Achieving new heights in object detection and image classification was made possible because of Convolution Neural Network(CNN). However, compared to image classification the object detection tasks are more difficult to analyze, more energy consuming and computation intensive. To overcome these challenges, a novel approach is developed for real time object detection applications to improve the accuracy and energy efficiency of the detection process. This is achieved by integrating the Convolutional Neural Networks (CNN) with the Scale Invariant Feature Transform (SIFT) algorithm. Here, we obtain high accuracy output with small sample data to train the model by integrating the CNN and SIFT features. The proposed detection model is a cluster of multiple deep convolutional neural networks and hybrid CNN-SIFT algorithm. The reason to use the SIFT featureis to amplify the model‟s capacity to detect small data or features as the SIFT requires small datasets to detect objects. Our simulation results show better performance in accuracy when compared with the conventional CNN method. As the resources like RAM, graphic card, ROM, etc. are limited we propose a pipelined implementation on an aggregate Central Processing Unit(CPU) and Graphical Processing Unit(GPU) platform.  

scholarly journals PLANET: Improved Convolutional Neural Networks with Image Enhancement for Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Chaohui Tang ◽  
Qingxin Zhu ◽  
Wenjun Wu ◽  
Wenlin Huang ◽  
Chaoqun Hong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Image Classification ◽  
Image Enhancement ◽  
Convolutional Neural Networks ◽  
Image Data ◽  
Training Samples ◽  
Enhancement Method ◽  
Deep Cnn ◽  
Classification Tasks

In the past few years, deep learning has become a research hotspot and has had a profound impact on computer vision. Deep CNN has been proven to be the most important and effective model for image processing, but due to the lack of training samples and huge number of learning parameters, it is easy to tend to overfit. In this work, we propose a new two-stage CNN image classification network, named “Improved Convolutional Neural Networks with Image Enhancement for Image Classification” and PLANET in abbreviation, which uses a new image data enhancement method called InnerMove to enhance images and augment the number of training samples. InnerMove is inspired by the “object movement” scene in computer vision and can improve the generalization ability of deep CNN models for image classification tasks. Sufficient experiment results show that PLANET utilizing InnerMove for image enhancement outperforms the comparative algorithms, and InnerMove has a more significant effect than the comparative data enhancement methods for image classification tasks.

scholarly journals Accurate BAPL Score Classification of Brain PET Images Based on Convolutional Neural Networks with a Joint Discriminative Loss Function †

2020 ◽  
Vol 10 (3) ◽  
pp. 965 ◽  
Author(s):  
Ryosuke Sato ◽  
Yutaro Iwamoto ◽  
Kook Cho ◽  
Do-Young Kang ◽  
Yen-Wei Chen
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Loss Function ◽  
Data Augmentation ◽  
Natural Image ◽  
Deep Convolutional Neural Networks ◽  
Cerebral Disease

Alzheimer’s disease (AD) is an irreversible progressive cerebral disease with most of its symptoms appearing after 60 years of age. Alzheimer’s disease has been largely attributed to accumulation of amyloid beta (Aβ), but a complete cure has remained elusive. 18F-Florbetaben amyloid positron emission tomography (PET) has been shown as a more powerful tool for understanding AD-related brain changes than magnetic resonance imaging and computed tomography. In this paper, we propose an accurate classification method for scoring brain amyloid plaque load (BAPL) based on deep convolutional neural networks. A joint discriminative loss function was formulated by adding a discriminative intra-loss function to the conventional (cross-entropy) loss function. The performance of the proposed joint loss function was compared with that of the conventional loss function in three state-of-the-art deep neural network architectures. The intra-loss function significantly improved the BAPL classification performance. In addition, we showed that the mix-up data augmentation method, originally proposed for natural image classification, was also useful for medical image classification.

scholarly journals DATA AUGMENTATION METHOD USING GENERATIVE ADVERSARIAL NETWORKS

2021 ◽  
pp. 83-91
Author(s):  
Oleksandr Chaikovskyi ◽  
Artem Volokyta ◽  
Artemi Kyrianov ◽  
Heorhii Loutskii
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Classical Image

The article discusses a data augmentation method based on generative adversarial networks to improve the accuracy of image classification by convolutional neural networks. A comparative analysis of the proposed method with classical image augmentation methods was performed.

scholarly journals Batch Similarity Based Triplet Loss Assembled into Light-Weighted Convolutional Neural Networks for Medical Image Classification

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 764
Author(s):  
Zhiwen Huang ◽  
Quan Zhou ◽  
Xingxing Zhu ◽  
Xuming Zhang
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Image Data ◽  
Classification Performance ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Triplet Loss ◽  
Medical Image Classification

In many medical image classification tasks, there is insufficient image data for deep convolutional neural networks (CNNs) to overcome the over-fitting problem. The light-weighted CNNs are easy to train but they usually have relatively poor classification performance. To improve the classification ability of light-weighted CNN models, we have proposed a novel batch similarity-based triplet loss to guide the CNNs to learn the weights. The proposed loss utilizes the similarity among multiple samples in the input batches to evaluate the distribution of training data. Reducing the proposed loss can increase the similarity among images of the same category and reduce the similarity among images of different categories. Besides this, it can be easily assembled into regular CNNs. To appreciate the performance of the proposed loss, some experiments have been done on chest X-ray images and skin rash images to compare it with several losses based on such popular light-weighted CNN models as EfficientNet, MobileNet, ShuffleNet and PeleeNet. The results demonstrate the applicability and effectiveness of our method in terms of classification accuracy, sensitivity and specificity.

Sign in / Sign up

Export Citation Format

Share Document