Log in

Relevant bibliographies by topics / Dilated convolution / Journal articles

To see the other types of publications on this topic, follow the link: Dilated convolution.

Journal articles on the topic 'Dilated convolution'

Author: Grafiati

Published: 24 August 2024

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Dilated convolution.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Wang, Wei, Yiyang Hu, Ting Zou, Hongmei Liu, Jin Wang, and Xin Wang. "A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/8817849.

Full text

Abstract:

Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilat

APA, Harvard, Vancouver, ISO, and other styles

2

Peng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong, and Benli Yu. "Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones." Journal of Physics: Conference Series 2246, no. 1 (2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.

Full text

Abstract:

Abstract In this paper, several improved convolutional recurrent networks (CRN) are proposed, which can enhance the speech with non-additive distortion captured by fiber-optic microphones. Our preliminary study shows that the original CRN structure based on amplitude spectrum estimation is seriously distorted due to the loss of phase information. Therefore, we transform the network to run in time domain and gain 0.42 improvement on PESQ and 0.03 improvement on STOI. In addition, we integrate dilated convolution into CRN architecture, and adopt three different types of bottleneck modules, namel

APA, Harvard, Vancouver, ISO, and other styles

3

Zhao, Feng, Junjie Zhang, Zhe Meng, and Hanqiang Liu. "Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification." Remote Sensing 13, no. 17 (2021): 3396. http://dx.doi.org/10.3390/rs13173396.

Full text

Abstract:

Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convo

APA, Harvard, Vancouver, ISO, and other styles

4

Chim, Seyha, Jin-Gu Lee, and Ho-Hyun Park. "Dilated Skip Convolution for Facial Landmark Detection." Sensors 19, no. 24 (2019): 5350. http://dx.doi.org/10.3390/s19245350.

Full text

Abstract:

Facial landmark detection has gained enormous interest for face-related applications due to its success in facial analysis tasks such as facial recognition, cartoon generation, face tracking and facial expression analysis. Many studies have been proposed and implemented to deal with the challenging problems of localizing facial landmarks from given images, including large appearance variations and partial occlusion. Studies have differed in the way they use the facial appearances and shape information of input images. In our work, we consider facial information within both global and local con

APA, Harvard, Vancouver, ISO, and other styles

5

Song, Zhendong, Yupeng Ma, Fang Tan, and Xiaoyi Feng. "Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement." Applied Sciences 12, no. 7 (2022): 3461. http://dx.doi.org/10.3390/app12073461.

Full text

Abstract:

In this paper, we propose a fully convolutional neural network based on recursive recurrent convolution for monaural speech enhancement in the time domain. The proposed network is an encoder-decoder structure using a series of hybrid dilated modules (HDM). The encoder creates low-dimensional features of a noisy input frame. In the HDM, the dilated convolution is used to expand the receptive field of the network model. In contrast, the standard convolution is used to make up for the under-utilized local information of the dilated convolution. The decoder is used to reconstruct enhanced frames.

APA, Harvard, Vancouver, ISO, and other styles

6

Tang, Jingfan, Meijia Zhou, Pengfei Li, Min Zhang, and Ming Jiang. "Crowd Counting Based on Multiresolution Density Map and Parallel Dilated Convolution." Scientific Programming 2021 (January 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/8831458.

Full text

Abstract:

The current crowd counting tasks rely on a fully convolutional network to generate a density map that can achieve good performance. However, due to the crowd occlusion and perspective distortion in the image, the directly generated density map usually neglects the scale information and spatial contact information. To solve it, we proposed MDPDNet (Multiresolution Density maps and Parallel Dilated convolutions’ Network) to reduce the influence of occlusion and distortion on crowd estimation. This network is composed of two modules: (1) the parallel dilated convolution module (PDM) that combines

APA, Harvard, Vancouver, ISO, and other styles

7

Yan, Yang, Liu Yang, and Wenbo Huang. "Fundus-DANet: Dilated Convolution and Fusion Attention Mechanism for Multilabel Retinal Fundus Image Classification." Applied Sciences 14, no. 18 (2024): 8446. http://dx.doi.org/10.3390/app14188446.

Full text

Abstract:

The difficulty of classifying retinal fundus images with one or more illnesses present or missing is known as fundus multi-lesion classification. The challenges faced by current approaches include the inability to extract comparable morphological features from images of different lesions and the inability to resolve the issue of the same lesion, which presents significant feature variances due to grading disparities. This paper proposes a multi-disease recognition network model, Fundus-DANet, based on the dilated convolution. It has two sub-modules to address the aforementioned issues: the int

APA, Harvard, Vancouver, ISO, and other styles

8

Cao, Ruifen, Xi Pei, Ning Ge, and Chunhou Zheng. "Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning." Technology in Cancer Research & Treatment 20 (January 1, 2021): 153303382110342. http://dx.doi.org/10.1177/15330338211034284.

Full text

Abstract:

Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-dependent. This paper introduces a deep dilated convolutional U-network to achieve fast and accurate clinical target volume auto-segmentation of esophageal cancer after radical surgery. The deep dilated convolutional U-network, which integrates the advantages of dilated convolution and the U-network, is an end-to-end architecture that enables rapid t

APA, Harvard, Vancouver, ISO, and other styles

9

Zhang, Jianming, Chaoquan Lu, Jin Wang, Lei Wang, and Xiao-Guang Yue. "Concrete Cracks Detection Based on FCN with Dilated Convolution." Applied Sciences 9, no. 13 (2019): 2686. http://dx.doi.org/10.3390/app9132686.

Full text

Abstract:

In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the residual network to extract the feature maps of the in

APA, Harvard, Vancouver, ISO, and other styles

10

Wang, Ran, Ruyu Shi, Xiong Hu, and Changqing Shen. "Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks." Shock and Vibration 2021 (January 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/6616861.

Full text

Abstract:

Remaining useful life (RUL) prediction is necessary for guaranteeing machinery’s safe operation. Among deep learning architectures, convolutional neural network (CNN) has shown achievements in RUL prediction because of its strong ability in representation learning. Features from different receptive fields extracted by different sizes of convolution kernels can provide complete information for prognosis. The single size convolution kernel in traditional CNN is difficult to learn comprehensive information from complex signals. Besides, the ability to learn local and global features synchronously

APA, Harvard, Vancouver, ISO, and other styles

11

Madych, W. R. "Limits of Dilated Convolution Transforms." SIAM Journal on Mathematical Analysis 16, no. 3 (1985): 551–58. http://dx.doi.org/10.1137/0516041.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Zhang, Guokai, Xiao Liu, Dandan Zhu, et al. "3D Spatial Pyramid Dilated Network for Pulmonary Nodule Classification." Symmetry 10, no. 9 (2018): 376. http://dx.doi.org/10.3390/sym10090376.

Full text

Abstract:

Lung cancer mortality is currently the highest among all kinds of fatal cancers. With the help of computer-aided detection systems, a timely detection of malignant pulmonary nodule at early stage could improve the patient survival rate efficiently. However, the sizes of the pulmonary nodules are usually various, and it is more difficult to detect small diameter nodules. The traditional convolution neural network uses pooling layers to reduce the resolution progressively, but it hampers the network’s ability to capture the tiny but vital features of the pulmonary nodules. To tackle this problem

APA, Harvard, Vancouver, ISO, and other styles

13

Heo, Woon-Haeng, Hyemi Kim, and Oh-Wook Kwon. "Source Separation Using Dilated Time-Frequency DenseNet for Music Identification in Broadcast Contents." Applied Sciences 10, no. 5 (2020): 1727. http://dx.doi.org/10.3390/app10051727.

Full text

Abstract:

We propose a source separation architecture using dilated time-frequency DenseNet for background music identification of broadcast content. We apply source separation techniques to the mixed signals of music and speech. For the source separation purpose, we propose a new architecture to add a time-frequency dilated convolution to the conventional DenseNet in order to effectively increase the receptive field in the source separation scheme. In addition, we apply different convolutions to each frequency band of the spectrogram in order to reflect the different frequency characteristics of the lo

APA, Harvard, Vancouver, ISO, and other styles

14

Contreras, Jonatan, Martine Ceberio, and Vladik Kreinovich. "Why Dilated Convolutional Neural Networks: A Proof of Their Optimality." Entropy 23, no. 6 (2021): 767. http://dx.doi.org/10.3390/e23060767.

Full text

Abstract:

One of the most effective image processing techniques is the use of convolutional neural networks that use convolutional layers. In each such layer, the value of the layer’s output signal at each point is a combination of the layer’s input signals corresponding to several neighboring points. To improve the accuracy, researchers have developed a version of this technique, in which only data from some of the neighboring points is processed. It turns out that the most efficient case—called dilated convolution—is when we select the neighboring points whose differences in both coordinates are divis

APA, Harvard, Vancouver, ISO, and other styles

15

Ülkü, İrem. "Effect of dilation rate on Nested U-Net model performance in remote sensing." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 67, no. 1 (2024): 27–42. https://doi.org/10.33769/aupse.1498035.

Full text

Abstract:

High spatial resolution remote sensing images contain substantial detailed multi-scale objects. Convolutional neural networks (CNNs) are not efficient enough for detecting these objects of varying sizes. Among the multitude of CNN approaches, the Nested U-Net (UNet++) model shows great potential to capture more complex details by progressively enriching highresolution feature maps. However, there is more room for improving the Nested U-Net architecture by increasing its ability to detect multi-scale objects. The nested blocks used in this architecture rely on standard convolutional layers, whi

APA, Harvard, Vancouver, ISO, and other styles

16

Viriyasaranon, Thanaporn, Seung-Hoon Chae, and Jang-Hwan Choi. "MFA-net: Object detection for complex X-ray cargo and baggage security imagery." PLOS ONE 17, no. 9 (2022): e0272961. http://dx.doi.org/10.1371/journal.pone.0272961.

Full text

Abstract:

Deep convolutional networks have been developed to detect prohibited items for automated inspection of X-ray screening systems in the transport security system. To our knowledge, the existing frameworks were developed to recognize threats using only baggage security X-ray scans. Therefore, the detection accuracy in other domains of security X-ray scans, such as cargo X-ray scans, cannot be ensured. We propose an object detection method for efficiently detecting contraband items in both cargo and baggage for X-ray security scans. The proposed network, MFA-net, consists of three plug-and-play mo

APA, Harvard, Vancouver, ISO, and other styles

17

Wu, Junjie, Wen Liu, and Yoshihisa Maruyama. "Automated Road-Marking Segmentation via a Multiscale Attention-Based Dilated Convolutional Neural Network Using the Road Marking Dataset." Remote Sensing 14, no. 18 (2022): 4508. http://dx.doi.org/10.3390/rs14184508.

Full text

Abstract:

Road markings, including road lanes and symbolic road markings, can convey abundant guidance information to autonomous driving cars. However, recent works have paid less attention to the recognition of symbolic road markings compared with road lanes. In this study, a road-marking-segmentation dataset named the RMD (Road Marking Dataset) is introduced to compensate for the lack of datasets and the limitations of the existing datasets. Furthermore, we propose a novel multiscale attention-based dilated convolutional neural network (MSA-DCNN) to tackle the proposed RMD. The proposed method employs

APA, Harvard, Vancouver, ISO, and other styles

18

Ku, Tao, Qirui Yang, and Hao Zhang. "Multilevel feature fusion dilated convolutional network for semantic segmentation." International Journal of Advanced Robotic Systems 18, no. 2 (2021): 172988142110076. http://dx.doi.org/10.1177/17298814211007665.

Full text

Abstract:

Recently, convolutional neural network (CNN) has led to significant improvement in the field of computer vision, especially the improvement of the accuracy and speed of semantic segmentation tasks, which greatly improved robot scene perception. In this article, we propose a multilevel feature fusion dilated convolution network (Refine-DeepLab). By improving the space pyramid pooling structure, we propose a multiscale hybrid dilated convolution module, which captures the rich context information and effectively alleviates the contradiction between the receptive field size and the dilated convol

APA, Harvard, Vancouver, ISO, and other styles

19

Zhuang, Zilong, Huichun Lv, Jie Xu, Zizhao Huang, and Wei Qin. "A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions." Applied Sciences 9, no. 9 (2019): 1823. http://dx.doi.org/10.3390/app9091823.

Full text

Abstract:

Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs. However, there are three primary concerns in the current research, namely real-time performance, effectiveness, and generalization performance. In this paper, a deep learning method based on stacked residual dilated convolutional neural network (SRDCNN) is proposed for real-time bearing fault diagnosis, which is subtly combined by the dilated convolution, the input gate structure of long short-term memory network (LSTM) and the resid

APA, Harvard, Vancouver, ISO, and other styles

20

Hu, Yicheng, Shufang Tian, and Jia Ge. "Hybrid Convolutional Network Combining Multiscale 3D Depthwise Separable Convolution and CBAM Residual Dilated Convolution for Hyperspectral Image Classification." Remote Sensing 15, no. 19 (2023): 4796. http://dx.doi.org/10.3390/rs15194796.

Full text

Abstract:

In recent years, convolutional neural networks (CNNs) have been increasingly leveraged for the classification of hyperspectral imagery, displaying notable advancements. To address the issues of insufficient spectral and spatial information extraction and high computational complexity in hyperspectral image classification, we introduce the MDRDNet, an integrated neural network model. This novel architecture is comprised of two main components: a Multiscale 3D Depthwise Separable Convolutional Network and a CBAM-augmented Residual Dilated Convolutional Network. The first component employs depthw

APA, Harvard, Vancouver, ISO, and other styles

21

Hu, Guoping, Fangzheng Zhao, and Bingqi Liu. "Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks." Remote Sensing 15, no. 12 (2023): 3117. http://dx.doi.org/10.3390/rs15123117.

Full text

Abstract:

This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signa

APA, Harvard, Vancouver, ISO, and other styles

22

Jin, Dawei, Ruizhi Kang, Hongjun Zhang, Wening Hao, and Gang Chen. "Improving Abstractive Summarization via Dilated Convolution." Journal of Physics: Conference Series 1616 (August 2020): 012078. http://dx.doi.org/10.1088/1742-6596/1616/1/012078.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Fang, Yuchun, Yifan Li, Xiaokang Tu, Taifeng Tan, and Xin Wang. "Face completion with Hybrid Dilated Convolution." Signal Processing: Image Communication 80 (February 2020): 115664. http://dx.doi.org/10.1016/j.image.2019.115664.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Zhao, Haixia, You Zhou, Tingting Bai, and Yuanzhong Chen. "A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression." Remote Sensing 15, no. 18 (2023): 4569. http://dx.doi.org/10.3390/rs15184569.

Full text

Abstract:

Seismic data processing plays a key role in the field of geophysics. The collected seismic data are inevitably contaminated by various types of noise, which makes the effective signals difficult to be accurately discriminated. A fundamental issue is how to improve the signal-to-noise ratio of seismic data. Due to the complex characteristics of noise and signals, it is a challenge for the denoising model to suppress noise and recover weak signals. To suppress random noise in seismic data, we propose a multi-scale deformable convolution neural network denoising model based on U-Net, named MSDC-U

APA, Harvard, Vancouver, ISO, and other styles

25

Lin, Yingjie, and Jianning Wu. "A Novel Multichannel Dilated Convolution Neural Network for Human Activity Recognition." Mathematical Problems in Engineering 2020 (July 11, 2020): 1–10. http://dx.doi.org/10.1155/2020/5426532.

Full text

Abstract:

A novel multichannel dilated convolution neural network for improving the accuracy of human activity recognition is proposed. The proposed model utilizes the multichannel convolution structure with multiple kernels of various sizes to extract multiscale features of high-dimensional data of human activity during convolution operation and not to consider the use of the pooling layers that are used in the traditional convolution with dilated convolution. Its advantage is that the dilated convolution can first capture intrinsical sequence information by expanding the field of convolution kernel wi

APA, Harvard, Vancouver, ISO, and other styles

26

Ma, Hao, Chao Chen, Qing Zhu, Haitao Yuan, Liming Chen, and Minglei Shu. "An ECG Signal Classification Method Based on Dilated Causal Convolution." Computational and Mathematical Methods in Medicine 2021 (February 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/6627939.

Full text

Abstract:

The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an automatic classification of ECG signals based on a dilated causal convolutional neural network. To solve the problem that the recurrent neural network framework network cannot be accelerated by hardware equipment, the dilated causal convolutional neural network is adopted. Given the features of the same input and output time steps of the recurrent n

APA, Harvard, Vancouver, ISO, and other styles

27

Rahman, Takowa, Md Saiful Islam, and Jia Uddin. "MRI-Based Brain Tumor Classification Using a Dilated Parallel Deep Convolutional Neural Network." Digital 4, no. 3 (2024): 529–54. http://dx.doi.org/10.3390/digital4030027.

Full text

Abstract:

Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) to better comprehend the spatial connections among pixels in complex pictures. Due to their tiny receptive fields, the majority of deep convolutional neural network (DCNN)-based techniques overfit and are unable to extract global context information from more significant regions. While dilated convolution retains data resolution at the output layer and increases the receptive field without adding computation, stacking several dilated convolutions has the drawback of producing a grid effect. Thi

APA, Harvard, Vancouver, ISO, and other styles

28

Khotimah, Wijayanti Nurul, Farid Boussaid, Ferdous Sohel, et al. "SC-CAN: Spectral Convolution and Channel Attention Network for Wheat Stress Classification." Remote Sensing 14, no. 17 (2022): 4288. http://dx.doi.org/10.3390/rs14174288.

Full text

Abstract:

Biotic and abiotic plant stress (e.g., frost, fungi, diseases) can significantly impact crop production. It is thus essential to detect such stress at an early stage before visual symptoms and damage become apparent. To this end, this paper proposes a novel deep learning method, called Spectral Convolution and Channel Attention Network (SC-CAN), which exploits the difference in spectral responses of healthy and stressed crops. The proposed SC-CAN method comprises two main modules: (i) a spectral convolution module, which consists of dilated causal convolutional layers stacked in a residual man

APA, Harvard, Vancouver, ISO, and other styles

29

Ni, Jian, Rui Wang, and Jing Tang. "ADSSD: Improved Single-Shot Detector with Attention Mechanism and Dilated Convolution." Applied Sciences 13, no. 6 (2023): 4038. http://dx.doi.org/10.3390/app13064038.

Full text

Abstract:

The detection of small objects is easily affected by background information, and a lack of context information makes detection difficult. Therefore, small object detection has become an extremely challenging task. Based on the above problems, we proposed a Single-Shot MultiBox Detector with an attention mechanism and dilated convolution (ADSSD). In the attention module, we strengthened the connection between information in space and channels while using cross-layer connections to accelerate training. In the multi-branch dilated convolution module, we combined three expansion convolutions with

APA, Harvard, Vancouver, ISO, and other styles

30

Xu, Jiawei, Jie Wu, Yu Lei, and Yuxiang Gu. "Application of Pseudo-Three-Dimensional Residual Network to Classify the Stages of Moyamoya Disease." Brain Sciences 13, no. 5 (2023): 742. http://dx.doi.org/10.3390/brainsci13050742.

Full text

Abstract:

It is essential to assess the condition of moyamoya disease (MMD) patients accurately and promptly to prevent MMD from endangering their lives. A Pseudo-Three-Dimensional Residual Network (P3D ResNet) was proposed to process spatial and temporal information, which was implemented in the identification of MMD stages. Digital Subtraction Angiography (DSA) sequences were split into mild, moderate and severe stages in accordance with the progression of MMD, and divided into a training set, a verification set, and a test set with a ratio of 6:2:2 after data enhancement. The features of the DSA imag

APA, Harvard, Vancouver, ISO, and other styles

31

Wang, Yanjie, Shiyu Hu, Guodong Wang, Chenglizhao Chen, and Zhenkuan Pan. "Multi-scale dilated convolution of convolutional neural network for crowd counting." Multimedia Tools and Applications 79, no. 1-2 (2019): 1057–73. http://dx.doi.org/10.1007/s11042-019-08208-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Wang, Yanjie, Guodong Wang, Chenglizhao Chen, and Zhenkuan Pan. "Multi-scale dilated convolution of convolutional neural network for image denoising." Multimedia Tools and Applications 78, no. 14 (2019): 19945–60. http://dx.doi.org/10.1007/s11042-019-7377-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Deng, Feiyue, Yan Bi, Yongqiang Liu, and Shaopu Yang. "Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network." Mathematics 9, no. 23 (2021): 3035. http://dx.doi.org/10.3390/math9233035.

Full text

Abstract:

Remaining useful life (RUL) prediction of key components is an important influencing factor in making accurate maintenance decisions for mechanical systems. With the rapid development of deep learning (DL) techniques, the research on RUL prediction based on the data-driven model is increasingly widespread. Compared with the conventional convolution neural networks (CNNs), the multi-scale CNNs can extract different-scale feature information, which exhibits a better performance in the RUL prediction. However, the existing multi-scale CNNs employ multiple convolution kernels with different sizes

APA, Harvard, Vancouver, ISO, and other styles

34

Ma, Tian, Xinlei Zhou, Jiayi Yang, et al. "Dental Lesion Segmentation Using an Improved ICNet Network with Attention." Micromachines 13, no. 11 (2022): 1920. http://dx.doi.org/10.3390/mi13111920.

Full text

Abstract:

Precise segmentation of tooth lesions is critical to creation of an intelligent tooth lesion detection system. As a solution to the problem that tooth lesions are similar to normal tooth tissues and difficult to segment, an improved segmentation method of the image cascade network (ICNet) network is proposed to segment various lesion types, such as calculus, gingivitis, and tartar. First, the ICNet network model is used to achieve real-time segmentation of lesions. Second, the Convolutional Block Attention Module (CBAM) is integrated into the ICNet network structure, and large-size convolution

APA, Harvard, Vancouver, ISO, and other styles

35

Nguyen, Tuan Anh, and Thanh Ngoc Tran. "Improving Short-Term Electrical Load Forecasting with Dilated Convolutional Neural Networks: A Comparative Analysis." Journal of Robotics and Control (JRC) 6, no. 2 (2025): 560–69. https://doi.org/10.18196/jrc.v6i2.24967.

Full text

Abstract:

Short-term load forecasting (STLF) is vital for grid stability and resource optimization for energy systems. Accurate forecasting helps maintain a stable power supply, reduce costs, and improve decision-making. Traditional convolutional neural networks (CNNs) capture local patterns well but struggle with long-term dependencies under fluctuating conditions. This study introduces an optimized Dilated Convolutional Neural Network (DCNN) to enhance accuracy in short- and long-term load forecasting. The key contribution is a new DCNN framework that expands the receptive field without adding computa

APA, Harvard, Vancouver, ISO, and other styles

36

Park, Sangun, and Dong Eui Chang. "Multipath Lightweight Deep Network Using Randomly Selected Dilated Convolution." Sensors 21, no. 23 (2021): 7862. http://dx.doi.org/10.3390/s21237862.

Full text

Abstract:

Robot vision is an essential research field that enables machines to perform various tasks by classifying/detecting/segmenting objects as humans do. The classification accuracy of machine learning algorithms already exceeds that of a well-trained human, and the results are rather saturated. Hence, in recent years, many studies have been conducted in the direction of reducing the weight of the model and applying it to mobile devices. For this purpose, we propose a multipath lightweight deep network using randomly selected dilated convolutions. The proposed network consists of two sets of multip

APA, Harvard, Vancouver, ISO, and other styles

37

Zhou, Bo, and Omer Saeed. "Comparative Analysis of Volleyball Serve Action Based on Human Posture Estimation." Mobile Information Systems 2022 (September 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/4817463.

Full text

Abstract:

Serving is one of the most crucial techniques in volleyball. Serving is a method that does not require team interaction and is difficult for the opponent to immediately interfere with. The feature migration module with a fixed offset is suggested in this work. This module can be thought of as a cross-channel dilated convolution approximation of dilated convolution. The reason cross-channel dilated convolution is not worse than standard dilated convolution with few parameters is discussed in this article. An improved random forest model is put forth to address the issue of the human pose estima

APA, Harvard, Vancouver, ISO, and other styles

38

Pentapati, Hema Kumar, and Sridevi K. "A Systematic Approach of Advanced Dilated Convolution Network for Speaker Identification." International Journal of Electrical and Electronics Research 11, no. 1 (2023): 25–30. http://dx.doi.org/10.37391/ijeer.110104.

Full text

Abstract:

Over the years, the Speaker recognition area is facing various challenges in identifying the speakers accurately. Remarkable changes came into existence with the advent of deep learning algorithms. Deep learning made a remarkable impact on the speaker recognition approaches. This paper introduces a simple novel architectural approach to an advanced Dilated Convolution network. The novel idea is to induce the well-structured log-Melspectrum to the proposed dilated convolution neural network and reduce the number of layers to 11. The network utilizes the Global average pooling to accumulate the

APA, Harvard, Vancouver, ISO, and other styles

39

Heo, Woon-Haeng, Hyemi Kim, and Oh-Wook Kwon. "Integrating Dilated Convolution into DenseLSTM for Audio Source Separation." Applied Sciences 11, no. 2 (2021): 789. http://dx.doi.org/10.3390/app11020789.

Full text

Abstract:

Herein, we proposed a multi-scale multi-band dilated time-frequency densely connected convolutional network (DenseNet) with long short-term memory (LSTM) for audio source separation. Because the spectrogram of the acoustic signal can be thought of as images as well as time series data, it is suitable for convolutional recurrent neural network (CRNN) architecture. We improved the audio source separation performance by applying the dilated block with a dilated convolution to CRNN architecture. The dilated block has the role of effectively increasing the receptive field in the spectrogram. In add

APA, Harvard, Vancouver, ISO, and other styles

40

Rhee, Jung-Soo. "CERTAIN RADIALLY DILATED CONVOLUTION AND ITS APPLICATION." Honam Mathematical Journal 32, no. 1 (2010): 101–12. http://dx.doi.org/10.5831/hmj.2010.32.1.101.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Orhei, Ciprian, Victor Bogdan, Cosmin Bonchis, and Radu Vasiu. "Dilated Filters for Edge-Detection Algorithms." Applied Sciences 11, no. 22 (2021): 10716. http://dx.doi.org/10.3390/app112210716.

Full text

Abstract:

Edges are a basic and fundamental feature in image processing that is used directly or indirectly in huge number of applications. Inspired by the expansion of image resolution and processing power, dilated-convolution techniques appeared. Dilated convolutions have impressive results in machine learning, so naturally we discuss the idea of dilating the standard filters from several edge-detection algorithms. In this work, we investigated the research hypothesis that use dilated filters, rather than the extended or classical ones, and obtained better edge map results. To demonstrate this hypothe

APA, Harvard, Vancouver, ISO, and other styles

42

Tran, Song-Toan, Thanh-Tuan Nguyen, Minh-Hai Le, Ching-Hwa Cheng, and Don-Gey Liu. "TDC-Unet: Triple Unet with Dilated Convolution for Medical Image Segmentation." International Journal of Pharma Medicine and Biological Sciences 11, no. 1 (2022): 1–7. http://dx.doi.org/10.18178/ijpmbs.11.1.1-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Ji, Changpeng, Haofeng Yu, and Wei Dai. "Network Traffic Anomaly Detection Based on Spatiotemporal Feature Extraction and Channel Attention." Processes 12, no. 7 (2024): 1418. http://dx.doi.org/10.3390/pr12071418.

Full text

Abstract:

To overcome the challenges of feature selection in traditional machine learning and enhance the accuracy of deep learning methods for anomaly traffic detection, we propose a novel method called DCGCANet. This model integrates dilated convolution, a GRU, and a Channel Attention Network, effectively combining dilated convolutional structures with GRUs to extract both temporal and spatial features for identifying anomalous patterns in network traffic. The one-dimensional dilated convolution (DC-1D) structure is designed to expand the receptive field, allowing for comprehensive traffic feature ext

APA, Harvard, Vancouver, ISO, and other styles

44

Yang, Hongbo, and Shi Qiu. "A Novel Dynamic Contextual Feature Fusion Model for Small Object Detection in Satellite Remote-Sensing Images." Information 15, no. 4 (2024): 230. http://dx.doi.org/10.3390/info15040230.

Full text

Abstract:

Ground objects in satellite images pose unique challenges due to their low resolution, small pixel size, lack of texture features, and dense distribution. Detecting small objects in satellite remote-sensing images is a difficult task. We propose a new detector focusing on contextual information and multi-scale feature fusion. Inspired by the notion that surrounding context information can aid in identifying small objects, we propose a lightweight context convolution block based on dilated convolutions and integrate it into the convolutional neural network (CNN). We integrate dynamic convolutio

APA, Harvard, Vancouver, ISO, and other styles

45

Bian, Shengqin, Xinyu He, Zhengguang Xu, and Lixin Zhang. "Hybrid Dilated Convolution with Attention Mechanisms for Image Denoising." Electronics 12, no. 18 (2023): 3770. http://dx.doi.org/10.3390/electronics12183770.

Full text

Abstract:

In the field of image denoising, convolutional neural networks (CNNs) have become increasingly popular due to their ability to learn effective feature representations from large amounts of data. In the field of image denoising, CNNs are widely used to improve performance. However, increasing network depth can weaken the influence of shallow layers on deep layers, especially for complex denoising tasks such as real denoising and blind denoising, where conventional networks fail to achieve high-quality results. To address this issue, this paper proposes a hybrid dilated convolution-based denoisi

APA, Harvard, Vancouver, ISO, and other styles

46

Zheng, Xinyu, Shengwei Pu, and Xingyu Xue. "ASCEND-UNet: An Improved UNet Configuration Optimized for Rural Settlements Mapping." Sensors 24, no. 17 (2024): 5453. http://dx.doi.org/10.3390/s24175453.

Full text

Abstract:

Different types of rural settlement agglomerations have been formed and mixed in space during the rural revitalization strategy implementation in China. Discriminating them from remote sensing images is of great significance for rural land planning and living environment improvement. Currently, there is a lack of automatic methods for obtaining information on rural settlement differentiation. In this paper, an improved encoder–decoder network structure, ASCEND-UNet, was designed based on the original UNet. It was implemented to segment and classify dispersed and clustered rural settlement buil

APA, Harvard, Vancouver, ISO, and other styles

47

Wang, Ji, Peiquan Xu, Leijun Li, and Feng Zhang. "DAssd-Net: A Lightweight Steel Surface Defect Detection Model Based on Multi-Branch Dilated Convolution Aggregation and Multi-Domain Perception Detection Head." Sensors 23, no. 12 (2023): 5488. http://dx.doi.org/10.3390/s23125488.

Full text

Abstract:

During steel production, various defects often appear on the surface of the steel, such as cracks, pores, scars, and inclusions. These defects may seriously decrease steel quality or performance, so how to timely and accurately detect defects has great technical significance. This paper proposes a lightweight model based on multi-branch dilated convolution aggregation and multi-domain perception detection head, DAssd-Net, for steel surface defect detection. First, a multi-branch Dilated Convolution Aggregation Module (DCAM) is proposed as a feature learning structure for the feature augmentati

APA, Harvard, Vancouver, ISO, and other styles

48

You, Jiangchuan, and Zhenhong Shang. "Solar Filament Detection Based on Improved DeepLab V3+." Publications of the Astronomical Society of the Pacific 134, no. 1036 (2022): 064501. http://dx.doi.org/10.1088/1538-3873/ac6e07.

Full text

Abstract:

Abstract A novel solar filament detection method based on an improved DeepLab V3+ is proposed to address the low detection accuracy of small solar filaments in Hα full-disk solar images. First, the Xception structure of the backbone network is fine-tuned, and the low-level feature information of the filaments is added to the decoder module of the network to improve the utilization of the solar filament features. Second, the receptive field of dilated convolution is expanded, and the information utilization rate is increased via cascaded dilated convolution to improve the detection accuracy of

APA, Harvard, Vancouver, ISO, and other styles

49

Ko, Tae-young, and Seung-ho Lee. "Novel Method of Semantic Segmentation Applicable to Augmented Reality." Sensors 20, no. 6 (2020): 1737. http://dx.doi.org/10.3390/s20061737.

Full text

Abstract:

This paper proposes a novel method of semantic segmentation, consisting of modified dilated residual network, atrous pyramid pooling module, and backpropagation, that is applicable to augmented reality (AR). In the proposed method, the modified dilated residual network extracts a feature map from the original images and maintains spatial information. The atrous pyramid pooling module places convolutions in parallel and layers feature maps in a pyramid shape to extract objects occupying small areas in the image; these are converted into one channel using a 1 × 1 convolution. Backpropagation com

APA, Harvard, Vancouver, ISO, and other styles

50

Yin, Deyang, Jinxin Wang, Kai Zhai, Jianfeng Zheng, and Hao Qiang. "Ground-Based Cloud Image Segmentation Method Based on Improved U-Net." Applied Sciences 14, no. 23 (2024): 11280. https://doi.org/10.3390/app142311280.

Full text

Abstract:

Cloud image segmentation is a technique that divides images captured by meteorological satellites or ground-based observations into different regions or categories. By extracting the distribution, shape, and dynamic features of clouds, it provides precise data support for the meteorological and environmental fields, significantly influencing photovoltaic (PV) power generation forecasting, astronomical telescope observatory site selection, and weather forecasting. A ground-based cloud image segmentation model based on an improved U-Net is proposed, which adopts an overall encoder–decoder struct

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!