scholarly journals Toward Efficient Image Recognition in Sensor-Based IoT: A Weight Initialization Optimizing Method for CNN Based on RGB Influence Proportion

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2866 ◽  
Author(s):  
Zile Deng ◽  
Yuanlong Cao ◽  
Xinyu Zhou ◽  
Yugen Yi ◽  
Yirui Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Image Data ◽  
Human Vision ◽  
Learning Technology ◽  
Complex Data ◽  
Training Process ◽  
Color Differences ◽  
Weight Initialization ◽  
The Internet Of Things

As the Internet of Things (IoT) is predicted to deal with different problems based on big data, its applications have become increasingly dependent on visual data and deep learning technology, and it is a big challenge to find a suitable method for IoT systems to analyze image data. Traditional deep learning methods have never explicitly taken the color differences of data into account, but from the experience of human vision, colors play differently significant roles in recognizing things. This paper proposes a weight initialization method for deep learning in image recognition problems based on RGB influence proportion, aiming to improve the training process of the learning algorithms. In this paper, we try to extract the RGB proportion and utilize it in the weight initialization process. We conduct several experiments on different datasets to evaluate the effectiveness of our proposal, and it is proven to be effective on small datasets. In addition, as for the access to the RGB influence proportion, we also provide an expedient approach to get the early proportion for the following usage. We assume that the proposed method can be used for IoT sensors to securely analyze complex data in the future.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

scholarly journals Studi Awal Deteksi COVID-19 Menggunakan Citra CT Berbasis Deep Learning

2020 ◽  
Vol 7 (3) ◽  
pp. 629
Author(s):  
Windra Swastika
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Ground Glass Opacity ◽  
Normal Subjects ◽  
Ct Images ◽  
Ground Glass ◽  
Virus Spread ◽  
Training Process ◽  
Specificity And Sensitivity ◽  
Preliminary Study

<p class="Abstrak">Pada bulan Desember 2019, virus COVID-19 menyebar ke banyak negara, termasuk di Indonesia yang kemudian menjadi pandemi dan menimbulkan masalah serius karena masih belum adanya vaksin untuk mencegah penularan. Uji spesimen saluran nafas atas dan saluran nafas bawah saat ini merupakan salah satu metode yang efektif untuk mengetahui apakah seseorang terinfeksi COVID-19 atau tidak. Salah satu indikasi dari infeksi COVID-19 adalah sesak nafas atau pneumonia serta munculnya <em>ground-glass opacity</em> pada citra CT. Penelitian ini merupakan studi awal untuk melihat apakah citra CT dari organ thorax dapat digunakan sebagai alternatif untuk mendeteksi infeksi virus COVID-19. Deep learning digunakan untuk membuat sebuah model dengan citra CT sebagai masukan. Total 140 data citra CT yang terbagi menjadi 2 yaitu citra dari pasien terinfeksi dan citra dari subjek normal digunakan sebagai masukan pada deep learning. Proses pelatihan dilakukan menggunakan CNN dengan arsitektur VGG16 dan optimizer SGD dan Adam. Hasil yang didapatkan adalah akurasi sebesar 92,86% untuk mengklasifikasikan infeksi COVID-19 dan normal. Nilai spesifisitas dan sensitivitas sebesar 100% dan 85,71% untuk pelatihan dengan menggunakan optimizer SGD.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>In December 2019, the COVID-19 virus spread to many countries, including Indonesia which later became a pandemic and caused serious problems because there was still no vaccine to prevent transmission. Tests of upper and lower respiratory tract specimens are now an effective method of finding whether a person is infected with COVID-19 or not. One indication of COVID-19 infection is shortness of breath or pneumonia and the appearance of ground-glass opacity on CT images. This research is a preliminary study to see whether CT images of the thorax organs can be used as an alternative to detect COVID-19 virus. The deep learning is used to create a model with CT images as input. A total of 140 CT image data which are divided into 2 images from infected patients and images from normal subjects are used as input for deep learning. The training process is carried out using CNN with VGG16 architecture and SGD and Adam optimizers. The results obtained are 92.86% accuracy for classifying COVID-19 infections and normal. Specificity and sensitivity values were 100% and 85.71% for training using the SGD optimizer.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Real-Time Object Detection For Wayang Punakawan Identification Using Deep Learning

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Afandi Nur Aziz Thohari ◽  
Rifki Adhitama
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Three Dimensional ◽  
Learning Technology ◽  
Single Shot ◽  
Training Process ◽  
Real Time System ◽  
Good Ability ◽  
Object Based ◽  
Wayang Kulit

Indonesia is a country that has a variety of cultures, one of which is wayang kulit. This typical javanese performance art must continue to be preserved so that to be known by future generations. There are many wayang figures in Indonesia, and the most famous is punakawan. Wayang punakawan consists of four character namely semar, gareng petruk, and bagong. To preserve wayang punakawan to be known by the next generation, then in this study created a system that is able to identify real-time punakawan object using deep learning technology. The method that used is Single Shot Multiple Detector (SSD) as one of the models of deep learning that has a good ability in classifying data with three-dimensional structures such as real-time video. SSD model with MobileNet layer can work in slight computation, so that it can be run in real-time system. To classify object there are two steps that must be done such as training process and testing process. Training process takes 28 hours with 100.000 steps of iteration.The result of training process is a model which used to identify object. Based on the test result obtained an accuracy to detect object was 98,86%. This prove that the system has been able to optimize object in real-time accurately.

scholarly journals A study on Anomaly Detection GAN-based methods on image data

2019 ◽  
Author(s):  
Emanuel Silva ◽  
Johannes Lochter
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Image Data ◽  
Original Method ◽  
Detection Task ◽  
Generative Adversarial Networks ◽  
High Dimensional ◽  
Complex Data ◽  
New Techniques ◽  
Adversarial Networks

The anomaly detection task is a well know problem being researched among a variety of areas, including machine learning. The task is to identify data patterns that have a non expected behaviour, that can be a malicious data sent by an attacker or a unexpected valid behaviour, in both cases the anomaly need to be identified. With the advance of deep learning based techniques showing that this class of algorithms can learn high-dimensional and complex data patterns, naturally it became an option to the anomaly detection task. Recent researches in literature are using a sub-field of deep learning algorithms named Generative Adversarial Networks for predicting anomalous samples, since the original method can learn the data distribution. These new techniques make some changes for the anomaly detection task, and this work provides a briefly review on these methods and provides a comparison with well known methods.

scholarly journals Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5128
Author(s):  
Shengzhe Wang ◽  
Bo Wang ◽  
Shifeng Wang ◽  
Yifeng Tang
Keyword(s):  
Deep Learning ◽  
Intelligent Systems ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Visual Saliency ◽  
Human Vision ◽  
Background Suppression ◽  
Training Process ◽  
Fusion Algorithm ◽  
Channel Expansion

Pedestrian detection is an important task in many intelligent systems, particularly driver assistance systems. Recent studies on pedestrian detection in infrared (IR) imagery have employed data-driven approaches. However, two problems in deep learning-based detection are the implicit performance and time-consuming training. In this paper, a novel channel expansion technique based on feature fusion is proposed to enhance the IR imagery and accelerate the training process. Besides, a novel background suppression method is proposed to stimulate the attention principle of human vision and shrink the region of detection. A precise fusion algorithm is designed to combine the information from different visual saliency maps in order to reduce the effect of truncation and miss detection. Four different experiments are performed from various perspectives in order to gauge the efficiency of our approach. The experimental results show that the Mean Average Precisions (mAPs) of four different datasets have been increased by 5.22% on average. The results prove that background suppression and suitable feature expansion will accelerate the training process and enhance the performance of IR image-based deep learning models.

scholarly journals Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yunong Tian ◽  
Guodong Yang ◽  
Zhe Wang ◽  
En Li ◽  
Zize Liang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Optical Sensors ◽  
Data Augmentation ◽  
Image Data ◽  
Disease Diagnosis ◽  
Lesion Detection ◽  
Training Data ◽  
Yield Reduction ◽  
Learning Technology

Plant disease is one of the primary causes of crop yield reduction. With the development of computer vision and deep learning technology, autonomous detection of plant surface lesion images collected by optical sensors has become an important research direction for timely crop disease diagnosis. In this paper, an anthracnose lesion detection method based on deep learning is proposed. Firstly, for the problem of insufficient image data caused by the random occurrence of apple diseases, in addition to traditional image augmentation techniques, Cycle-Consistent Adversarial Network (CycleGAN) deep learning model is used in this paper to accomplish data augmentation. These methods effectively enrich the diversity of training data and provide a solid foundation for training the detection model. In this paper, on the basis of image data augmentation, densely connected neural network (DenseNet) is utilized to optimize feature layers of the YOLO-V3 model which have lower resolution. DenseNet greatly improves the utilization of features in the neural network and enhances the detection result of the YOLO-V3 model. It is verified in experiments that the improved model exceeds Faster R-CNN with VGG16 NET, the original YOLO-V3 model, and other three state-of-the-art networks in detection performance, and it can realize real-time detection. The proposed method can be well applied to the detection of anthracnose lesions on apple surfaces in orchards.

Deep Learning for the Detection and Recognition of Rail Defects in Ultrasound B-Scan Images

2021 ◽  
pp. 036119812110215
Author(s):  
Zhengxing Chen ◽  
Qihang Wang ◽  
Kanghua Yang ◽  
Tianle Yu ◽  
Jidong Yao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Defect Detection ◽  
High Performance ◽  
Image Data ◽  
Small Scale ◽  
Detection Accuracy ◽  
Data Set ◽  
Detection Rates ◽  
B Scan Images

Rail defect detection is crucial to rail operations safety. Addressing the problem of high false alarm rates and missed detection rates in rail defect detection, this paper proposes a deep learning method using B-scan image recognition of rail defects with an improved YOLO (you only look once) V3 algorithm. Specifically, the developed model can automatically position a box in B-scan images and recognize EFBWs (electric flash butt welds), normal bolt holes, BHBs (bolt hole breaks), and SSCs (shells, spalling, or corrugation). First, the network structure of the YOLO V3 model is modified to enlarge the receptive field of the model, thus improving the detection accuracy of the model for small-scale objects. Second, B-scan image data are analyzed and standardized. Third, the initial training parameters of the improved YOLO V3 model are adjusted. Finally, the experiments are performed on 453 B-scan images as the test data set. Results show that the B-scan image recognition model based on the improved YOLO V3 algorithm reached high performance in its precision. Additionally, the detection accuracy and efficiency are improved compared with the original model and the final mean average precision can reach 87.41%.

scholarly journals Deep Learning Frameworks for Rapid Gram Stain Image Data Interpretation: Protocol for a Retrospective Data Analysis (Preprint)

2020 ◽  
Author(s):  
Hee Kim ◽  
Thomas Ganslandt ◽  
Thomas Miethke ◽  
Michael Neumaier ◽  
Maximilian Kittel
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Image Interpretation ◽  
Image Data ◽  
Data Interpretation ◽  
Routine Care ◽  
Fine Tuning ◽  
Learning Technology ◽  
Gram Stain ◽  
Computational Performance

BACKGROUND In recent years, remarkable progress has been made in deep learning technology and successful use cases have been introduced in the medical domain. However, not many studies have considered high-performance computing to fully appreciate the capability of deep learning technology. OBJECTIVE This paper aims to design a solution to accelerate an automated Gram stain image interpretation by means of a deep learning framework without additional hardware resources. METHODS We will apply and evaluate 3 methodologies, namely fine-tuning, an integer arithmetic–only framework, and hyperparameter tuning. RESULTS The choice of pretrained models and the ideal setting for layer tuning and hyperparameter tuning will be determined. These results will provide an empirical yet reproducible guideline for those who consider a rapid deep learning solution for Gram stain image interpretation. The results are planned to be announced in the first quarter of 2021. CONCLUSIONS Making a balanced decision between modeling performance and computational performance is the key for a successful deep learning solution. Otherwise, highly accurate but slow deep learning solutions can add value to routine care. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/16843

scholarly journals Experimental Discussion on Fire Image Recognition Based on Deep Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012071
Author(s):  
Yongyi Cui ◽  
Fang Qu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Rapid Development ◽  
Fire Detection ◽  
Learning Technology ◽  
Fire Recognition

Abstract Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

scholarly journals Implementation of Convolutional Neural Network to Realize a Real Time Emotion based Music Player

2019 ◽  
Vol 8 (2S11) ◽  
pp. 164-171
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Data Science ◽  
Internal Representation ◽  
Learning Technology ◽  
Complex Data ◽  
Scale Invariant ◽  
Two Dimensional Image ◽  
Music Player

The ability to represent the world as a nested hierarchy of concepts, by defining each concept in relation to abstract representations has promoted deep learning to be widely used as a processing model for solving data science tasks. The era of digitalization has allowed the deep learning technology to flourish and machines with the ability to analyse huge amount of complex data would now be able to give progressively exact outcomes due to its supremacy in terms of accuracy when trained with massive amount of data. Convolutional Neural Networks(CNN), being a deep neural network with their ability to develop an internal representation of a two-dimensional image, allows the model to learn position and scale invariant structures in the data, which is important when working with images. For realizing emotion aware applications, the system must be highly accurate and in real time. In this paper, we provide the design and implementation details of a real time emotion based music player using CNN with the aim to reduce human effort and invoke the feasibility of Human Computer interaction(HCI).

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