Andry Maykol Pinto

1.1k total citations
71 papers, 667 citations indexed

About

Andry Maykol Pinto is a scholar working on Computer Vision and Pattern Recognition, Ocean Engineering and Aerospace Engineering. According to data from OpenAlex, Andry Maykol Pinto has authored 71 papers receiving a total of 667 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Computer Vision and Pattern Recognition, 29 papers in Ocean Engineering and 27 papers in Aerospace Engineering. Recurrent topics in Andry Maykol Pinto's work include Robotics and Sensor-Based Localization (27 papers), Underwater Vehicles and Communication Systems (23 papers) and Robotic Path Planning Algorithms (16 papers). Andry Maykol Pinto is often cited by papers focused on Robotics and Sensor-Based Localization (27 papers), Underwater Vehicles and Communication Systems (23 papers) and Robotic Path Planning Algorithms (16 papers). Andry Maykol Pinto collaborates with scholars based in Portugal, Spain and United States. Andry Maykol Pinto's co-authors include Anı́bal Matos, António Paulo Moreira, Paulo Costa, Pedro Nuno Leite, Daniel Campos, Luís F. Rocha, Miguel Velhote Correia, Giovanni Fasano, Emilio F. Campana and Germano Veiga and has published in prestigious journals such as IEEE Access, Sensors and IEEE Transactions on Aerospace and Electronic Systems.

In The Last Decade

Andry Maykol Pinto

66 papers receiving 634 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Andry Maykol Pinto Portugal 15 270 248 216 86 84 71 667
Max Pfingsthorn Germany 18 408 1.5× 481 1.9× 249 1.2× 86 1.0× 106 1.3× 61 816
Geert De Cubber Belgium 12 318 1.2× 366 1.5× 67 0.3× 99 1.2× 89 1.1× 74 656
Qingwu Li China 16 391 1.4× 171 0.7× 152 0.7× 38 0.4× 52 0.6× 71 946
Yonghoon Ji Japan 17 292 1.1× 271 1.1× 136 0.6× 124 1.4× 36 0.4× 71 671
Liang Yang China 14 243 0.9× 102 0.4× 115 0.5× 61 0.7× 75 0.9× 46 615
Riccardo Polvara United Kingdom 9 315 1.2× 349 1.4× 143 0.7× 145 1.7× 19 0.2× 16 687
Armando Alves Neto Brazil 17 456 1.7× 408 1.6× 255 1.2× 230 2.7× 26 0.3× 53 783
Christoforos Kanellakis Sweden 14 521 1.9× 552 2.2× 74 0.3× 156 1.8× 75 0.9× 66 816
Milena F. Pinto Brazil 17 342 1.3× 326 1.3× 68 0.3× 220 2.6× 54 0.6× 101 843
Brian Coltin United States 15 332 1.2× 286 1.2× 40 0.2× 78 0.9× 68 0.8× 53 819

Countries citing papers authored by Andry Maykol Pinto

Since Specialization
Citations

This map shows the geographic impact of Andry Maykol Pinto'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 Andry Maykol Pinto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andry Maykol Pinto more than expected).

Fields of papers citing papers by Andry Maykol Pinto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andry Maykol Pinto. 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 Andry Maykol Pinto. The network helps show where Andry Maykol Pinto may publish in the future.

Co-authorship network of co-authors of Andry Maykol Pinto

This figure shows the co-authorship network connecting the top 25 collaborators of Andry Maykol Pinto. A scholar is included among the top collaborators of Andry Maykol Pinto 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 Andry Maykol Pinto. Andry Maykol Pinto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Pinto, Andry Maykol, et al.. (2025). PerceptNet-V2X: Perception Network for Vehicle to Everything Scenarios in Autonomous Driving. IEEE Access. 13. 182645–182660.
2.
Leite, Pedro Nuno & Andry Maykol Pinto. (2025). Multimodal information fusion using pyramidal attention-based convolutions for underwater tri-dimensional scene reconstruction. Information Fusion. 124. 103339–103339.
3.
Campilho, R.D.S.G., et al.. (2025). Raya: A Bio-Inspired AUV for Inspection and Intervention of Underwater Structures. IEEE Access. 13. 71253–71274.
4.
Pinto, Andry Maykol, et al.. (2025). Anomaly Detection for PV Modules Using Multi-Modal Data Fusion in Aerial Inspections. IEEE Access. 13. 88762–88779. 2 indexed citations
5.
Leite, Pedro Nuno, et al.. (2024). Hybrid underwater imaging for the tri-dimensional inspection of critical structural elements in offshore platforms. Ocean Engineering. 314. 119658–119658. 2 indexed citations
6.
Pinto, Andry Maykol, et al.. (2023). Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review. ISPRS International Journal of Geo-Information. 12(7). 260–260. 36 indexed citations
7.
Campos, Daniel, et al.. (2023). Shore Control Centre for Multi-Domain Heterogeneous Robotic Vehicles. 1–4. 1 indexed citations
8.
Leite, Pedro Nuno & Andry Maykol Pinto. (2023). Fusing heterogeneous tri-dimensional information for reconstructing submerged structures in harsh sub-sea environments. Information Fusion. 103. 102126–102126. 10 indexed citations
9.
10.
Campos, Daniel, Anı́bal Matos, & Andry Maykol Pinto. (2022). Modular Multi-Domain Aware Autonomous Surface Vehicle for Inspection. IEEE Access. 10. 113355–113375. 10 indexed citations
11.
Campilho, R.D.S.G., et al.. (2022). Application of a Design for Excellence Methodology for a Wireless Charger Housing in Underwater Environments. Machines. 10(4). 232–232. 7 indexed citations
12.
Leite, Pedro Nuno, et al.. (2021). Advancing Autonomous Surface Vehicles: A 3D Perception System for the Recognition and Assessment of Docking-Based Structures. IEEE Access. 9. 53030–53045. 26 indexed citations
13.
Leite, Pedro Nuno & Andry Maykol Pinto. (2021). Exploiting Motion Perception in Depth Estimation Through a Lightweight Convolutional Neural Network. IEEE Access. 9. 76056–76068. 10 indexed citations
14.
Cruz, Nuno, et al.. (2020). Altitude control of an underwater vehicle based on computer vision. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 2 indexed citations
15.
Leite, Pedro Nuno, et al.. (2020). Detecting Docking-based Structures for Persistent ASVs using a Volumetric Neural Network. Global Oceans 2020: Singapore – U.S. Gulf Coast. 1–6. 5 indexed citations
16.
17.
Cruz, Nuno, José Almeida, Rui M. Almeida, et al.. (2017). Cooperative deep water seafloor mapping with heterogeneous robotic platforms. 9 indexed citations
18.
Lima, José, Ana I. Pereira, Paulo Costa, Andry Maykol Pinto, & Pedro Costa. (2017). A fast and robust kinematic model for a 12 DoF hyper-redundant robot positioning: An optimization proposal. AIP conference proceedings. 1863. 270007–270007. 3 indexed citations
19.
Pinto, Andry Maykol, Paulo Costa, & António Paulo Moreira. (2014). An architecture for visual motion perception of a surveillance-based autonomous robot. Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT). 10. 205–211. 7 indexed citations
20.
Campana, Emilio F., Giovanni Fasano, & Andry Maykol Pinto. (2006). Dynamic system analysis and initial particles position in Particle Swarm Optimization. 32 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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