scholarly journals Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3633
Author(s):  
Rytis Augustauskas ◽  
Arūnas Lipnickas ◽  
Tadas Surgailis
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Proposed Model ◽  
Drilling Quality ◽  
Drilling Operations ◽  
Spatial Pyramid Pooling ◽  
Automated Object Detection ◽  
Wooden Furniture ◽  
Product Assembly ◽  
Small Bump

Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly. Faults in the process might introduce adverse effects to the furniture. Inspection of the drilling quality can be challenging due to a big variety of board surface textures, dust, or woodchips in the manufacturing process, milling cutouts, and other kinds of defects. Intelligent computer vision methods can be engaged for global contextual analysis with local information attention for automated object detection and segmentation. In this paper, we propose blind and through drilled holes segmentation on textured wooden furniture panel images using the UNet encoder-decoder modifications enhanced with residual connections, atrous spatial pyramid pooling, squeeze and excitation module, and CoordConv layers for better segmentation performance. We show that even a lightweight architecture is capable to perform on a range of complex textures and is able to distinguish the holes drilling operations’ semantical information from the rest of the furniture board and conveyor context. The proposed model configurations yield better results in more complex cases with a not significant or small bump in processing time. Experimental results demonstrate that our best-proposed solution achieves a Dice score of up to 97.89% compared to the baseline U-Net model’s Dice score of 94.50%. Statistical, visual, and computational properties of each convolutional neural network architecture are addressed.

scholarly journals Multiscale Road Extraction in Remote Sensing Images

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Aziguli Wulamu ◽  
Zuxian Shi ◽  
Dezheng Zhang ◽  
Zheyu He
Keyword(s):  
Remote Sensing ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Proposed Model ◽  
Different Types ◽  
Spatial Pyramid Pooling ◽  
The One

Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.

Security Model of Internet of Things Based on Binary Wavelet and Sparse Neural Network

2019 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Zhihui Wang ◽  
Jingjing Yang ◽  
Benzhen Guo ◽  
Xiao Zhang
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Network Architecture ◽  
Safety Management ◽  
The Internet ◽  
Security Model ◽  
The Novel ◽  
Simulated Environment ◽  
Proposed Model ◽  
The Internet Of Things

At present, the internet of things has no standard system architecture. According to the requirements of universal sensing, reliable transmission, intelligent processing and the realization of human, human and the material, real-time communication between objects and things, the internet needs the open, hierarchical, extensible network architecture as the framework. The sensation equipment safe examination platform supports the platform through the open style scene examination to measure the equipment and provides the movement simulated environment, including each kind of movement and network environment and safety management center, turning on application gateway supports. It examines the knowledge library. Under this inspiration, this article proposes the novel security model based on the sparse neural network and wavelet analysis. The experiment indicates that the proposed model performs better compared with the other state-of-the-art algorithms.

scholarly journals A Deep Convolutional Neural Network Architecture for Cancer Diagnosis using Histopathological Images

2021 ◽  
Vol 10 (12) ◽  
pp. 7-12
Author(s):  
Karthika Gidijala ◽  
◽  
Mansa Devi Pappu ◽  
Manasa Vavilapalli ◽  
Mahesh Kothuru ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Proposed Model ◽  
Histopathological Images

Many different models of Convolution Neural Networks exist in the Deep Learning studies. The application and prudence of the algorithms is known only when they are implemented with strong datasets. The histopathological images of breast cancer are considered as to have much number of haphazard structures and textures. Dealing with such images is a challenging issue in deep learning. Working on wet labs and in coherence to the results many research have blogged with novel annotations in the research. In this paper, we are presenting a model that can work efficiently on the raw images with different resolutions and alleviating with the problems of the presence of the structures and textures. The proposed model achieves considerably good results useful for decision making in cancer diagnosis.

scholarly journals Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction

2021 ◽  
Vol 11 (18) ◽  
pp. 8628
Author(s):  
Kang-Moon Park ◽  
Donghoon Shin ◽  
Sung-Do Chi
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Simulation Studies ◽  
Neural Network Architecture ◽  
Conventional Model ◽  
Neural Architecture ◽  
The Neural Network ◽  
Proposed Model ◽  
Improve Cost

This paper proposes a deep neural network structuring methodology through a genetic algorithm (GA) using chromosome non-disjunction. The proposed model includes methods for generating and tuning the neural network architecture without the aid of human experts. Since the original neural architecture search (henceforth, NAS) was announced, NAS techniques, such as NASBot, NASGBO and CoDeepNEAT, have been widely adopted in order to improve cost- and/or time-effectiveness for human experts. In these models, evolutionary algorithms (EAs) are employed to effectively enhance the accuracy of the neural network architecture. In particular, CoDeepNEAT uses a constructive GA starting from minimal architecture. This will only work quickly if the solution architecture is small. On the other hand, the proposed methodology utilizes chromosome non-disjunction as a new genetic operation. Our approach differs from previous methodologies in that it includes a destructive approach as well as a constructive approach, and is similar to pruning methodologies, which realizes tuning of the previous neural network architecture. A case study applied to the sentence word ordering problem and AlexNet for CIFAR-10 illustrates the applicability of the proposed methodology. We show from the simulation studies that the accuracy of the model was improved by 0.7% compared to the conventional model without human expert.

scholarly journals Suggestion and invention of recipes using bi-directional LSTMs-based frameworks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sai Nikhil Rao Gona ◽  
Himamsu Marellapudi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Loss Function ◽  
Recurrent Neural Networks ◽  
Network Architecture ◽  
Loss Functions ◽  
Neural Network Architecture ◽  
Calorie Content ◽  
Proposed Model ◽  
Variational Autoencoder

AbstractChoosing which recipe to eat and which recipe to avoid isn’t that simple for anyone. It takes strenuous efforts and a lot of time for people to calculate the number of calories and P.H level of the dish. In this paper, we propose an ensemble neural network architecture that suggests recipes based on the taste of the person, P.H level and calorie content of the recipes. We also propose a bi-directional LSTMs-based variational autoencoder for generating new recipes. We have ensembled three bi-directional LSTM-based recurrent neural networks which can classify the recipes based on the taste of the person, P.H level of the recipe and calorie content of the recipe. The proposed model also predicts the taste ratings of the recipes for which we proposed a custom loss function which gave better results than the standard loss functions and the model also predicts the calorie content of the recipes. The bi-directional LSTMs-based variational autoencoder after being trained with the recipes which are fit for the person generates new recipes from the existing recipes. After training and testing the recurrent neural networks and the variational autoencoder, we have tested the model with 20 new recipes and got overwhelming results in the experimentation, the variational autoencoders generated a couple of new recipes, which are healthy to the specific person and will be liked by the specific person.

scholarly journals DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

2017 ◽  
Author(s):  
Huifeng Guo ◽  
Ruiming TANG ◽  
Yunming Ye ◽  
Zhenguo Li ◽  
Xiuqiang He
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Feature Learning ◽  
High Order ◽  
Feature Engineering ◽  
Neural Network Architecture ◽  
User Behaviors ◽  
Feature Interactions ◽  
Deep Model ◽  
Proposed Model

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.

Multi-feature Fusion of Deep Neural Network for Screening Atrial Fibrillation Using ECG Signals

2021 ◽  
Author(s):  
Xingxiang Tao ◽  
Hao Dang ◽  
Xiangdong Xu ◽  
Xiaoguang Zhou ◽  
Danqun Xiong
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Diagnostic Tools ◽  
Data Set ◽  
Ecg Signals ◽  
Proposed Model ◽  
Unseen Data

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and it can cause a variety of cardiovascular diseases. This brings great hidden danger to people’s health and life safety all over the world. Electrocardiogram (ECG) is one of the most important noninvasive diagnostic tools for heart disease. Accurate interpretation of ECG is particularly important for the detection and treatment of AF. It is valuable to develop an efficient, accurate, and stable automatic AF detection algorithm in clinical settings. Therefore, this article proposes a novel integrated module, which combines densely connected convolutional network (DenseNet) module and bidirectional long short-term memory (BLSTM) module, based on the excellent ability of BLSTM on extracting the time series features, while DenseNet on capturing local features. Furthermore, we also propose a novel network architecture (MF-DenseNet–BLSTM) based on the integrated module mentioned above and multi-feature fusion for automatic AF detection using the ECG signals. The proposed model employs the architecture of dual-stream deep neural network to fusing multiple features. Specifically, the network of each stream structure consists of two parts with DenseNet module and BLSTM module. The data sets used to validate and test the proposed model are from the MIT-BIH Atrial Fibrillation Database. The experimental results show that the proposed model achieved 98.81% accuracy in training set, and achieved 98.04% accuracy in the testing set which is unseen data set. The proposed MF-DenseNet–BLSTM has shown excellent robustness and accuracy in automatic AF detection.

scholarly journals Automatic Bridge Crack Detection Using a Convolutional Neural Network

2019 ◽  
Vol 9 (14) ◽  
pp. 2867 ◽  
Author(s):  
Hongyan Xu ◽  
Xiu Su ◽  
Yi Wang ◽  
Huaiyu Cai ◽  
Kerang Cui ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Transportation Safety ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Convolutional Network ◽  
Detection Model ◽  
Proposed Model ◽  
Spatial Pyramid Pooling

Concrete bridge crack detection is critical to guaranteeing transportation safety. The introduction of deep learning technology makes it possible to automatically and accurately detect cracks in bridges. We proposed an end-to-end crack detection model based on the convolutional neural network (CNN), taking the advantage of atrous convolution, Atrous Spatial Pyramid Pooling (ASPP) module and depthwise separable convolution. The atrous convolution obtains a larger receptive field without reducing the resolution. The ASPP module enables the network to extract multi-scale context information, while the depthwise separable convolution reduces computational complexity. The proposed model achieved a detection accuracy of 96.37% without pre-training. Experiments showed that, compared with traditional classification models, the proposed model has a better performance. Besides, the proposed model can be embedded in any convolutional network as an effective feature extraction structure.

scholarly journals Multi-Column Atrous Convolutional Neural Network for Counting Metro Passengers

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 682
Author(s):  
Jun Zhang ◽  
Gaoyi Zhu ◽  
Zhizhong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Excellent Performance ◽  
Multi Scale ◽  
Density Maps ◽  
Deep Feature ◽  
Proposed Model ◽  
Spatial Pyramid Pooling ◽  
Deep Feature Extraction ◽  
Symmetric Method

We propose a symmetric method of accurately estimating the number of metro passengers from an individual image. To this end, we developed a network for metro-passenger counting called MPCNet, which provides a data-driven and deep learning method of understanding highly congested scenes and accurately estimating crowds, as well as presenting high-quality density maps. The proposed MPCNet is composed of two major components: A deep convolutional neural network (CNN) as the front end, for deep feature extraction; and a multi-column atrous CNN as the back-end, with atrous spatial pyramid pooling (ASPP) to deliver multi-scale reception fields. Existing crowd-counting datasets do not adequately cover all the challenging situations considered in our work. Therefore, we collected specific subway passenger video to compile and label a large new dataset that includes 346 images with 3475 annotated heads. We conducted extensive experiments with this and other datasets to verify the effectiveness of the proposed model. Our results demonstrate the excellent performance of the proposed MPCNet.

scholarly journals Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2557 ◽  
Author(s):  
Rytis Augustauskas ◽  
Arūnas Lipnickas
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Quality Evaluation ◽  
Model Comparison ◽  
Neural Network Architecture ◽  
Computational Overhead ◽  
Spatial Pyramid Pooling ◽  
Inspection Tasks ◽  
Defect Extraction ◽  
Speed And Accuracy

Convolutional neural networks perform impressively in complicated computer-vision image-segmentation tasks. Vision-based systems surpass humans in speed and accuracy in quality inspection tasks. Moreover, the maintenance of big infrastructures, such as roads, bridges, or buildings, is tedious and time-demanding work. In this research, we addressed pavement-quality evaluation by pixelwise defect segmentation using a U-Net deep autoencoder. Additionally, to the original neural network architecture, we utilized residual connections, atrous spatial pyramid pooling with parallel and “Waterfall” connections, and attention gates to perform better defect extraction. The proposed neural network configurations showed a segmentation performance improvement over U-Net with no significant computational overhead. Statistical and visual performance evaluation was taken into consideration for the model comparison. Experiments were conducted on CrackForest, Crack500, GAPs384, and mixed datasets.

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