Zobrazeno 1 - 10
of 2 116
pro vyhledávání: '"Separable Convolution"'
Publikováno v:
Dianxin kexue, Vol 40, Pp 42-51 (2024)
Aiming at the problems of poor quality of restored images and large number of model parameters in the current occluded face image restoration, a lightweight face image restoration model based on multi-scale feature fusion with improved U-Net, LM-UNET
Externí odkaz:
https://doaj.org/article/14e8c04d60444b3a92ed0b889937997c
Publikováno v:
智慧农业, Vol 6, Iss 4, Pp 42-52 (2024)
ObjectiveThe monitoring of livestock grazing in natural pastures is a key aspect of the transformation and upgrading of large-scale breeding farms. In order to meet the demand for large-scale farms to achieve accurate real-time detection of a large n
Externí odkaz:
https://doaj.org/article/15a75a289c254469ac1cd54b5f653d32
Publikováno v:
Journal of Applied Science and Engineering, Vol 27, Iss 8, Pp 2961-2969 (2024)
Image classification tasks often compress the neural network model to reduce the number of parameters, which leads to a decrease in classification accuracy. herefore, we propose a novel ResNet50-based attention mechanism for image classification. Res
Externí odkaz:
https://doaj.org/article/77a51823cec341c5b490368b27b86078
Autor:
Mengni Fu, Chi Lu, Yingwu Mao, Xiaoli Zhang, Yong Wu, Hongbin Luo, Zhi Liu, Wenfang Li, Guanglong Ou
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTAddressing accuracy and computational complexity challenges in hyperspectral image classification for small sample and multi-species scenarios, we developed DSC-DC, a lightweight convolutional neural network. This model is based on Depthwise
Externí odkaz:
https://doaj.org/article/7a17dce18f8b4685980832ce40ba340e
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
IntroductionIn response to the current mainstream deep learning detection methods with a large number of learned parameters and the complexity of apple leaf disease scenarios, the paper proposes a lightweight method and names it LCGSC-YOLO. This meth
Externí odkaz:
https://doaj.org/article/46603865ad2f47fa898b7e9842b69e1c
Publikováno v:
Buletin Pos dan Telekomunikasi: Media Komunikasi Ilmiah, Vol 22, Iss 1, Pp 54-74 (2024)
Identity theft, a pervasive criminal risk in the digital realm, particularly in online transactions, demands innovative security solutions. Voice biometrics, a cutting-edge technology, have been developed to ensure the protection of one's identificat
Externí odkaz:
https://doaj.org/article/a50ad1c2ec0744218fff648fec33ad2a
Publikováno v:
Information Processing in Agriculture, Vol 11, Iss 2, Pp 202-216 (2024)
In unstructured environments, dense grape fruit growth and the presence of occlusion cause difficult recognition problems, which will seriously affect the performance of grape picking robots. To address these problems, this study improves the YOLOX-T
Externí odkaz:
https://doaj.org/article/301376e520204ccdaa38541423d209c5
Publikováno v:
BMC Oral Health, Vol 24, Iss 1, Pp 1-17 (2024)
Abstract Background Oral mucosal diseases are similar to the surrounding normal tissues, i.e., their many non-salient features, which poses a challenge for accurate segmentation lesions. Additionally, high-precision large models generate too many par
Externí odkaz:
https://doaj.org/article/3a9aa74498db424bbca60891a9327cb4
Publikováno v:
Engineering Science and Technology, an International Journal, Vol 61, Iss , Pp 101930- (2025)
Recently, with the quick development of industrial equipment automation, convolutional neural networks (CNN) have been broadly applied to the intelligent fault diagnosis of rolling bearings. In order to solve the problems of gradient vanishing, gradi
Externí odkaz:
https://doaj.org/article/3cf60a81e60b46f2b3b34c40300a463b
Autor:
Lei Zhang, Shuang Zhao, Guanchao Zhao, Lingyi Wang, Baolin Liu, Zhimin Na, Zhijian Liu, Zhongming Yu, Wei He
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
In response to the issue of short-term fluctuations in photovoltaic (PV) output due to cloud movement, this paper proposes a method for forecasting short-term PV output based on a Depthwise Separable Convolution Visual Geometry Group (DSCVGG) and a D
Externí odkaz:
https://doaj.org/article/9fbaa9d51ff642aaa89787232ec83112