Francesco Salvetti

694 total citations
11 papers, 439 citations indexed

About

Francesco Salvetti is a scholar working on Computer Vision and Pattern Recognition, Plant Science and Artificial Intelligence. According to data from OpenAlex, Francesco Salvetti has authored 11 papers receiving a total of 439 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 4 papers in Plant Science and 3 papers in Artificial Intelligence. Recurrent topics in Francesco Salvetti's work include Smart Agriculture and AI (4 papers), Robotics and Sensor-Based Localization (3 papers) and Indoor and Outdoor Localization Technologies (2 papers). Francesco Salvetti is often cited by papers focused on Smart Agriculture and AI (4 papers), Robotics and Sensor-Based Localization (3 papers) and Indoor and Outdoor Localization Technologies (2 papers). Francesco Salvetti collaborates with scholars based in Italy, United Kingdom and Belgium. Francesco Salvetti's co-authors include Marcello Chiaberge, Vittorio Mazzia, Simone Angarano, Aleem Khaliq, Federico Angelini and E. Picciotto and has published in prestigious journals such as IEEE Access, Sensors and Pattern Recognition.

In The Last Decade

Francesco Salvetti

10 papers receiving 427 citations

Peers

Francesco Salvetti

CVPR

AI

PS

BE

EEE

Mohd Nizam Husen Malaysia

Rin-ichiro Taniguchi Japan

Simone Angarano Italy

KangGeon Kim South Korea

Rin‐ichiro Taniguchi Japan

Moataz M. Abdelwahab Egypt

Anhar Risnumawan Indonesia

Victor Eruhimov United States

Mohd Nizam Husen Malaysia

Francesco Salvetti

90 ×0.4

CVPR

91 ×0.8

AI

46 ×0.5

PS

22 ×0.3

BE

86 ×1.3

EEE

Citations per year, relative to Francesco Salvetti Francesco Salvetti (= 1×) peers Mohd Nizam Husen

Countries citing papers authored by Francesco Salvetti

Since Specialization

Citations

This map shows the geographic impact of Francesco Salvetti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Francesco Salvetti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco Salvetti more than expected).

Fields of papers citing papers by Francesco Salvetti

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Francesco Salvetti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Francesco Salvetti. The network helps show where Francesco Salvetti may publish in the future.

Co-authorship network of co-authors of Francesco Salvetti

This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Salvetti. A scholar is included among the top collaborators of Francesco Salvetti based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Francesco Salvetti. Francesco Salvetti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Angarano, Simone, et al.. (2024). Back-to-Bones: Rediscovering the role of backbones in domain generalization. Pattern Recognition. 156. 110762–110762. 6 indexed citations

2.

Angarano, Simone, et al.. (2023). Deep Instance Segmentation and Visual Servoing to Play Jenga with a Cost-Effective Robotic System. Sensors. 23(2). 752–752.

3.

Angarano, Simone, et al.. (2023). Generative Adversarial Super-Resolution at the edge with knowledge distillation. Engineering Applications of Artificial Intelligence. 123. 106407–106407. 18 indexed citations

4.

Salvetti, Francesco, et al.. (2022). Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learning. 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). 477–484. 31 indexed citations

5.

Mazzia, Vittorio, et al.. (2021). An Adaptive Row Crops Path Generator with Deep Learning Synergy. 6–12. 1 indexed citations

6.

Mazzia, Vittorio, et al.. (2021). DeepWay: A Deep Learning waypoint estimator for global path generation. Computers and Electronics in Agriculture. 184. 106091–106091. 16 indexed citations

7.

Mazzia, Vittorio, Simone Angarano, Francesco Salvetti, Federico Angelini, & Marcello Chiaberge. (2021). Action Transformer: A self-attention model for short-time pose-based human action recognition. Pattern Recognition. 124. 108487–108487. 176 indexed citations

8.

Angarano, Simone, et al.. (2021). Robust ultra-wideband range error mitigation with deep learning at the edge. Engineering Applications of Artificial Intelligence. 102. 104278–104278. 44 indexed citations

9.

Mazzia, Vittorio, et al.. (2020). Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband. arXiv (Cornell University). 4 indexed citations

10.

Mazzia, Vittorio, Aleem Khaliq, Francesco Salvetti, & Marcello Chiaberge. (2020). Real-Time Apple Detection System Using Embedded Systems With Hardware Accelerators: An Edge AI Application. IEEE Access. 8. 9102–9114. 142 indexed citations

11.

Picciotto, E. & Francesco Salvetti. (1956). Comptage d’α à basse temperature; une méthode pour éviter la diffusion du radon. Il Nuovo Cimento. 3(4). 815–819. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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