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IEEE Access, 2021 Disease is one of the main factors affecting crop growth. How to reflect the external morphological features of the disease and completely retain the color and texture information of the disease area is one of the key research issues for crop disease ...
Yuxia Yuan, Zengyong Xu, Gang Lu
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A Study on Defect Detection Model of Bone Plates Using Multiple Filter CNN of Parallel Structure [PDF]
한국정밀공학회지, 2023 Bone plates are a medical device used for fixing broken bones, which should not have a crack and hole defect. Defect detection is very important because bone plate defect is very dangerous.
Song Yeon Lee, Yong Jeong Huh
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Contextual Convolutional Neural Networks [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the standard convolution, which is the core component of convolutional neural networks. CoConv is implicitly equipped with the capability of incorporating contextual information while maintaining a similar number of parameters and computational cost ...
Ionut Cosmin Duta +2 more
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Research Progress of Lightweight Neural Network Convolution Design [PDF]
Jisuanji kexue yu tansuo, 2022 Traditional neural networks have the disadvantages of over-reliance on hardware resources and high requirements for application equipment performance. Therefore, they cannot be deployed on edge devices and mobile terminals with limited computing power ...
MA Jinlin, ZHANG Yu, MA Ziping, MAO Kaiji
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Orthogonal Features Extraction Method and Its Application in Convolution Neural Network
Shanghai Jiaotong Daxue xuebao, 2021 In view of feature redundancy in the convolutional neural network, the concept of orthogonal vectors is introduced into features. Then, a method for orthogonal features extraction of convolutional neural network is proposed from the perspective of ...
LI Chen, LI Jianxun
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Lightweight Convolutional Neural Network Architecture for Mobile Platforms [PDF]
Jisuanji gongcheng, 2019 For the problem that the deep neural network has low accuracy and over-fitting on the mobile platforms,a lightweight Convolutional Neural Network(CNN) architecture is proposed.The 3×3 depthwise separable convolution replaces the standard 3× ...
HU Ting,ZHU Yongxin,TIAN Li,FENG Songlin,WANG Hui
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Pansharpening by Convolutional Neural Networks [PDF]
Remote Sensing, 2016 A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices ...
MASI, GIUSEPPE +3 more
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Simplicial Convolutional Neural Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction.
Maosheng Yang, Elvin Isufi, Geert Leus
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Exploring Underwater Target Detection Algorithm Based on Improved SSD
Xibei Gongye Daxue Xuebao, 2020 As the in-depth exploration of oceans continues, the accurate and rapid detection of fish, bionics and other intelligent bodies in an underwater environment is more and more important for improving an underwater defense system.
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Control Application of Wolf Group Optimization Convolutional Neural Network in Ship Virtual Manufacturing [PDF]
Jisuanji kexue, 2021 In order to optimize the control strategy of virtual industrial manufacturing,the convolution neural network algorithm based on wolf swarm optimization is used to study the control of virtual industrial manufacturing.Firstly,according to the task and ...
XIAO Shi-long, WU Di, TANG Chao-chen, SHEN Xian-hao, ZHANG De-yu
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