Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Vittorio Mazzia"'
Autor:
Simone Cerrato, Vittorio Mazzia, Francesco Salvetti, Mauro Martini, Simone Angarano, Alessandro Navone, Marcello Chiaberge
Publikováno v:
IEEE Access, Vol 12, Pp 138306-138318 (2024)
Expensive sensors and inefficient algorithmic pipelines significantly affect the overall cost of autonomous machines. However, affordable robotic solutions are essential to practical usage, and their financial impact constitutes a fundamental require
Externí odkaz:
https://doaj.org/article/1562c42904bd4ce1847c5f062ec5732e
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Abstract Deep convolutional neural networks, assisted by architectural design strategies, make extensive use of data augmentation techniques and layers with a high number of feature maps to embed object transformations. That is highly inefficient and
Externí odkaz:
https://doaj.org/article/8c49880da55c4f129745f80880ff3925
Publikováno v:
IEEE Access, Vol 8, Pp 9102-9114 (2020)
Real-time apple detection in orchards is one of the most effective ways of estimating apple yields, which helps in managing apple supplies more effectively. Traditional detection methods used highly computational machine learning algorithms with inte
Externí odkaz:
https://doaj.org/article/428258569f9f4957a522422b44f86d7f
Publikováno v:
Remote Sensing, Vol 13, Iss 13, p 2564 (2021)
The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-at
Externí odkaz:
https://doaj.org/article/1dfd99026908436bafaa4fd3cf13ad5d
Publikováno v:
Machines, Vol 8, Iss 3, p 49 (2020)
The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools t
Externí odkaz:
https://doaj.org/article/f20bba4da57b4c3191b66a5ad358da5d
Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks
Publikováno v:
Remote Sensing, Vol 12, Iss 14, p 2207 (2020)
Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image Super-resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data. Ho
Externí odkaz:
https://doaj.org/article/d1ebe1f1f41543a183f0bf3e2a6bed05
Publikováno v:
Machines, Vol 8, Iss 2, p 27 (2020)
With the advent of agriculture 3.0 and 4.0, in view of efficient and sustainable use of resources, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation a
Externí odkaz:
https://doaj.org/article/6f4156d0eb4f4e2f9af795fcb67ce163
Publikováno v:
Sensors, Vol 20, Iss 9, p 2530 (2020)
Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated descr
Externí odkaz:
https://doaj.org/article/3e6a8a921bc84c9392916d36761771b3
Publikováno v:
Applied Sciences, Vol 10, Iss 1, p 238 (2019)
Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite im
Externí odkaz:
https://doaj.org/article/301f425db2314929b055a03428bfe9aa
Autor:
Simone Angarano, Francesco Salvetti, Vittorio Mazzia, Giovanni Fantin, Dario Gandini, Marcello Chiaberge
Publikováno v:
Lecture Notes in Networks and Systems ISBN: 9783031104633
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8cd53a9cdf3a30ff20f7659dc2568265
https://doi.org/10.1007/978-3-031-10464-0_56
https://doi.org/10.1007/978-3-031-10464-0_56
