A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit
- Journal
- Electronics
In The Last Decade
doi.org/10.3390/electronics10030279 →Countries where authors are citing A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit
This map shows the geographic impact of A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit. 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 A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit more than expected).
Fields of papers citing A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit
This network shows the impact of A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit.
About A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit
This paper, published in 2021, received 425 indexed citations . Written by R. Padilla, Wesley L. Passos, Sérgio L. Netto and Eduardo A. B. da Silva covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (187 citations), Plant Science (56 citations) and Artificial Intelligence (48 citations). Published in Electronics.
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.
This paper is also available at doi.org/10.3390/electronics10030279.