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Application of Optimized Convolution Neural Network Model in Mural Segmentation

Zhiqiang Chen, Leelavathi Rajamanickam, Xiaodong Tian, Jianfang Cao, Soumya Ranjan Nayak
2022 Applied Computational Intelligence and Soft Computing  
+ MobileNetV2) that fuses lightweight convolutional neural networks is proposed.  ...  The model combines the Deeplabv3+ structure and MobileNetV2 network and adopts the unique spatial pyramid structure of DeeplabV3+ to process convolutional features for multi-scale fusion, which reduces  ...  After 10 generations, the overall accuracy of the experiments and the test set training accuracy gradually increased and stabilized at the 40th generation, while the learning rate reached the optimum.  ... 
doi:10.1155/2022/5485117 fatcat:mzy6qxqonrctfc6smljqsmd7mm

Automated segmentaiton and classification of arterioles and venules using Cascading Dilated Convolutional Neural Networks [article]

Meng Li, Yan Zhang, Haicheng She, Jinqiong Zhou, Jia Jia, Danmei He, Li Zhang
2018 arXiv   pre-print
In this work, we propose a novel architecture of deep convolutional neural network for segmenting and classifying arterioles and venules on retinal fundus images.  ...  To improve the classification accuracy, we develop a special encoding path that couples InceptionV4 modules and Cascading Dilated Convolutions (CDCs) on top of the backbone network.  ...  We propose a novel architecture of deep convolutional neural network for segmenting and classifying arterioles and venules on retinal fundus images.  ... 
arXiv:1812.00137v1 fatcat:ih2swsrw5ffsniivmdlmm7nhwm

Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

Weidong Song, Guohui Jia, Hong Zhu, Di Jia, Lin Gao
2020 Journal of Advanced Transportation  
To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection  ...  Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.  ...  Using a top-down convolutional neural network can identify the target region with strong discrimination, but for the target region with weak discrimination, the classi cation performance is reduced [33  ... 
doi:10.1155/2020/6412562 fatcat:aeqx3iy44bhilm2ggq67qlxgze

Prediction of Epileptic EEG Signal Based on SECNN-LSTM

Jian Qiang Wang, Wei Fang, Victor S. Sheng
2022 Journal of New Media  
We propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete  ...  Brain-Computer Interface (BCI) technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real life.  ...  Acknowledgement: This work was supported by the National Natural Science Foundation of China (Grant No. 42075007), and the Open Grants of the State Key Laboratory of Severe Weather (No. 2021LASW-B19).  ... 
doi:10.32604/jnm.2022.027040 fatcat:27elx7lxpzemvfu3edc2o6knna

An Effective Multi-Scale Feature Network for Detecting Connector Solder Joint Defects

Kaihua Zhang, Haikuo Shen
2022 Machines  
With the rapid development of industry, people's requirements for the functionality, stability, and safety of electronic products are becoming higher and higher.  ...  In this paper, we propose a multi-level feature detection network based on multi-level feature maps fusion and feature enhancement for detecting connector solder joints, classifying and locating qualified  ...  The network uses a shallow segmentation network with a lightweight convolutional neural network for pixel-level crack detection, and its accuracy of defect detection reaches 98.5%, with the product of  ... 
doi:10.3390/machines10020094 fatcat:onnzogd7mffopk4b5nrfotexey

Image Denoising with Control over Deep Network Hallucination

Liang Qiyuan, Cassayre Florian, Owsianko Haley, El Helou Majed, Süsstrunk Sabine
2022 IS&T International Symposium on Electronic Imaging Science and Technology  
We propose to fuse the two components with a frequencydomain approach that takes into account the reliability of the deep network outputs.  ...  For better control and interpretability over a deep denoiser, we propose a novel framework exploiting a denoising network. We call it controllable confidence-based image denoising (CCID).  ...  The input image is denoised in parallel with a convolution filter and with a deep neural network, and the final output is obtained by fusing the two results.  ... 
doi:10.2352/ei.2022.34.14.coimg-217 fatcat:5lfhpcw5tvhjrn6w2unwrh7o3u

Automatic Detection of Invasive Ductal Carcinoma Based on the Fusion of Multi-Scale Residual Convolutional Neural Network and SVM

Jianfei Zhang, Xiaoyan Guo, Bo Wang, Wensheng Cui
2021 IEEE Access  
This paper proposes a method for the automatic detection of IDC based on the fusion of multi-scale residual convolutional neural network (MSRCNN) and SVM.  ...  Pathological analysis of biopsy is the gold standard for diagnosis of breast cancer, and early detection, diagnosis, and treatment can significantly increase the survival rate.  ...  This paper starts with a network model containing 1 MSRC block, and sequentially search for the network model with the highest classification accuracy.  ... 
doi:10.1109/access.2021.3063803 fatcat:ldao33xunbgwjdszvfvkytohoy

SDF-SLAM: A Deep Learning based Highly Accurate SLAM using Monocular Camera aiming at Indoor Map Reconstruction with Semantic and Depth Fusion

Chen Yang, Qi Chen, Yaoyao Yang, Jingyu Zhang, Minshun Wu, Kuizhi Mei
2022 IEEE Access  
thirdly, monocular based SLAM builds a fused map of feature points that lacks semantic information, which is incomprehensible for machine.  ...  The results show that the average accuracy of the predicted point cloud coordinates reaches 90%, and the average accuracy of the semantic labels reaches 67%.  ...  through a convolutional neural network.  ... 
doi:10.1109/access.2022.3144845 fatcat:v77xmzqjtzecbhlcijm44c7jfm

NTIRE 2020 Challenge on Image and Video Deblurring

Seungjun Nah, Sanghyun Son, Radu Timofte, Kyoung Mu Lee, Yu Tseng, Yu-Syuan Xu, Cheng-Ming Chiang, Yi-Min Tsai, Stephan Brehm, Sebastian Scherer, Dejia Xu, Yihao Chu (+36 others)
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy.  ...  Track 1 aims to develop single-image deblurring methods focusing on restoration quality.  ...  ., DisneyResearch|Studios, and ETH Zurich (Computer Vision Lab).  ... 
doi:10.1109/cvprw50498.2020.00216 dblp:conf/cvpr/NahSTLTXCTBSXCS20 fatcat:a6ojyfuidrbb3avwdpv4mje77e

Study of Human Motion Recognition Algorithm Based on Multichannel 3D Convolutional Neural Network

Yang Ju, Zhihan Lv
2021 Complexity  
Thirdly, a multichannel 3D convolutional neural network is proposed, and the multiple information extracted by the network is fused to form an output recognizer.  ...  Aiming at the problem that it is difficult to balance the speed and accuracy of human behaviour recognition, this paper proposes a method of motion recognition based on random projection.  ...  Improved Action Recognition Algorithm of 3D Convolutional Neural Network. e core of the 3D convolutional neural network constructed in this paper uses a dual-stream 3D convolutional neural network, and  ... 
doi:10.1155/2021/7646813 fatcat:ynqu4ky5szgebjo67pz6n4i3qq

An Enhanced Double-Filter Deep Residual Neural Network for Generating Super Resolution DEMs

Annan Zhou, Yumin Chen, John P. Wilson, Heng Su, Zhexin Xiong, Qishan Cheng
2021 Remote Sensing  
An enhanced double-filter deep residual neural network (EDEM-SR) method is proposed, which uses filters with different receptive field sizes to fuse and extract features and reconstruct a more realistic  ...  Inspired by the convolution neural network (CNN) excellent performance in super-resolution (SR) image analysis, this paper investigates the use of deep residual neural networks and low-resolution DEMs  ...  The function of these residual modules is to increase the depth of the network, and further extract and fuse the feature maps generated by filters with different receptive fields.  ... 
doi:10.3390/rs13163089 fatcat:e6eo2ddcrvfvtl63ev4withaae

Pavement crack detection algorithm based on densely connected and deeply supervised network

Haifeng Li, Jianping Zong, Jingjing Nie, Zhilong Wu, Hongyang Han
2021 IEEE Access  
In order to improve the accuracy and robustness of existing automated crack detection methods, a fully convolutional neural network for pixel-level detection based on densely connected and deeply supervised  ...  In addition, a class-balanced cross-entropy loss function is designed to balance backgrounds and cracks by increasing the weight of crack pixel loss.  ...  In addition, with growing depth of neural network structures and increasing number of layers, the extraction of crack feature could be more difficult, and the gradients are going to vanishing.  ... 
doi:10.1109/access.2021.3050401 fatcat:g7xawojpdzdplcilrn5klenf7y

Evaluation of Enterprise Financial Risk Level under Digital Transformation with Artificial Neural Network

Dijie Yang, Muhammad Arif
2022 Security and Communication Networks  
At the same time, the depth-wise separable convolution (DSConv) structure in Mobile-Net is combined with the ResNet network to build a lightweight deep neural network.  ...  Combining it with artificial neural networks, this work proposes an intelligent method for assessing the financial risk level of enterprises in this context.  ...  Two common pooling methods are max pooling and average pooling. e fully connected layer flattens the input into a column vector and fuses the features extracted by the entire network. e neural network  ... 
doi:10.1155/2022/1882100 fatcat:icyfmmsgyvgbfkyzjfxpatah7i

Improved Feature Pyramid Convolutional Neural Network for Effective Recognition of Music Scores

Lei Li, Qiangyi Li
2022 Computational Intelligence and Neuroscience  
An improved convolutional neural network for musical score recognition is proposed in this paper.  ...  Because the traditional convolutional neural network SEGNET misclassifies some pixels, this paper employs the feature pyramid structure.  ...  As a result, it is critical to continue researching OMR algorithms with increased resilience and accuracy.  ... 
doi:10.1155/2022/6071114 pmid:35586087 pmcid:PMC9110142 fatcat:7hkjks3etfe3reksjkzpsk7mna

DS-MENet for the classification of citrus disease

Xuyao Liu, Yaowen Hu, Guoxiong Zhou, Weiwei Cai, Mingfang He, Jialei Zhan, Yahui Hu, Liujun Li
2022 Frontiers in Plant Science  
In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters.  ...  Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure.  ...  Acknowledgments We are grateful to all members of the Food Academy of Central South University of Forestry and Technology for their advice and assistance in the course of this research. frontiersin.org  ... 
doi:10.3389/fpls.2022.884464 pmid:35937334 pmcid:PMC9355402 fatcat:pek4jvihivagtjxvvoted6biyy
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