Zobrazeno 1 - 10
of 1 035
pro vyhledávání: '"DeeplabV3+"'
Publikováno v:
Automatika, Vol 65, Iss 3, Pp 1177-1190 (2024)
The domain of deep learning has seen significant advancements, particularly in the context of detecting macular edema from images of the retina, in recent times. This study introduces an innovative model for identifying macular edema, employing two d
Externí odkaz:
https://doaj.org/article/5c3999ae757c42108755cb32b0d3e54a
Publikováno v:
Shanghai Jiaotong Daxue xuebao, Vol 58, Iss 5, Pp 776-782 (2024)
Aiming at the problem that mobile cleaning robot needs to identify road accurately and quickly when it operates in photovoltaic plants, a target recognition model of improved DeepLabv3+ to identify the roads within photovoltaic plants is proposed. Fi
Externí odkaz:
https://doaj.org/article/97d0ce9213f244fe98991fb88852a52c
Publikováno v:
Cogent Food & Agriculture, Vol 10, Iss 1 (2024)
AbstractAgricultural modernization urgently requires precise control of pear tree leaf diseases, with accurate identification and segmentation of disease spots becoming crucial aspects for ensuring crop health. Addressing potential issues such as mis
Externí odkaz:
https://doaj.org/article/2c3edf1b5f2345c18720fb649f914b6e
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 130, Iss , Pp 103895- (2024)
Surface melt plays a vital role in impacting the polar mass balance and global sea level rise. Over the past decades, synthetic aperture radar (SAR) imagery has garnered considerable attention due to its capacity to provide high-precision and long-te
Externí odkaz:
https://doaj.org/article/2fff1377da9b44f292eb3efa64d860e1
Publikováno v:
Ecological Indicators, Vol 162, Iss , Pp 112061- (2024)
Despite water ecosystems being capable of sustaining biodiversity and enhancing the overall resilience of the urban environment, they are highly susceptible to biological invasions. Invasive aquatic plants (IAPs) threaten the natural environment by r
Externí odkaz:
https://doaj.org/article/140cf71b980c4db5be611d83cfe07088
Autor:
P. Anilkumar, Dimitar Tokmakov, P. Venugopal, Srinivas Koppu, Nevena Mileva, Anna Bekyarova-Tokmakova
Publikováno v:
IEEE Access, Vol 12, Pp 147723-147738 (2024)
A multi-objective derived Adaptive TransDeepLabv3 (ATransDeeplabv3) with a meta-heuristic approach Electric Fish optimization (EFO) Algorithm is proposed in this paper. The Deeplabv3 network’s segmentation results are unsatisfactory when it comes t
Externí odkaz:
https://doaj.org/article/dc1fbf50b97f4d418b9a6da8351fb84e
Autor:
Tanzina Akter Tani, Jelena Tesic
Publikováno v:
IEEE Access, Vol 12, Pp 141280-141290 (2024)
Retinal vessel segmentation is crucial for the diagnosis and monitoring of ophthalmic illnesses. Deep learning algorithms have been extensively utilized in automated segmentation to improve effectiveness and efficiency. In this paper, we introduce th
Externí odkaz:
https://doaj.org/article/328f3d052ea74b6cb9a1a0a7a0e03d49
Publikováno v:
IEEE Access, Vol 12, Pp 87397-87406 (2024)
Road segmentation is an important task in the field of semantic segmentation, and the Deeplabv3+ algorithm, which is commonly used for road segmentation, has shortcomings, such as numerous parameters and a tendency to lose detailed information. There
Externí odkaz:
https://doaj.org/article/003c47f79cd747fd910f33e02e639ef2
Publikováno v:
IEEE Access, Vol 12, Pp 69445-69455 (2024)
Multi-spectral satellite imagery has been widely used for land cover classification, because it provides meaningful spectral information for Earth’s objects that are difficult to be described by using visible band images. The near-infrared image en
Externí odkaz:
https://doaj.org/article/c6c0f1d63f9d4afc9a00b32f864c5b55
Publikováno v:
IEEE Access, Vol 12, Pp 52067-52085 (2024)
Accurate segmentation of river water in close-range Remote Sensing (RS) images is vital for efficient environmental monitoring and management. However, this task poses significant difficulties due to the dynamic nature of water, which exhibits varyin
Externí odkaz:
https://doaj.org/article/33d0f9bdb73d413994e8e91fefba0e17