{rfName}
Pe

Indexed in

APC

1 996,00 Euros
IEEE
Transformative agreement with library

License and Use

Icono OpenAccess

Citations

13

Altmetrics

Analysis of institutional authors

Llorente, AlvaroAuthorDel Rio, AlbertoCorresponding AuthorSerrano, JavierAuthorJimenez, DavidAuthor

Share

July 29, 2024
Publications
>
Article
Hybrid Gold

Performance Evaluation of YOLOv8-Based Bib Number Detection in Media Streaming Race

Publicated to: IEEE TRANSACTIONS ON BROADCASTING. 70 (3): 1126-1138 - 2024-09-01 70(3), DOI: 10.1109/tbc.2024.3414656

Authors:

Martinez, Rafael; Llorente, Alvaro; del Rio, Alberto; Serrano, Javier; Jimenez, David
[+]

Affiliations

Univ Poliecn Madrid, Escuela Tecn Super Ingn Telecomunicac ETSIT, Signals Syst & Radiocommun Dept, Madrid 28040, Spain - Author
Univ Politecn Madrid, Escuela Tecn Super Ingn Sistemas Informat ETSISI, Informat Syst Dept, Madrid 28031, Spain - Author
Univ Politecn Madrid, Escuela Tecn Super Ingn Telecomunicac ETSIT, Phys Elect Elect Engn & Appl Phys Dept, Madrid 28040, Spain - Author
See more

Abstract

The evolution of telecommunication networks unlocks new possibilities for multimedia services, including enriched and personalized experiences. However, ensuring high Quality of Service and Quality of Experience requires intelligent solutions at the edge. This study investigates the real-time detection of race bib numbers using YOLOv8, a state-of-the-art object detection framework, within the context of 5G/6G edge computing. We train (BDBD and SVHN datasets) and analyze various YOLOv8 models (nano to extreme) across two diverse racing datasets (TGCRBNW and RBNR), encompassing varied environmental conditions (daytime and nighttime). Our assessment focuses on key performance metrics, including processing time, efficiency, and accuracy. For instance, on the TGCRBNW dataset, the extreme-sized model shows a noticeable reduction in prediction time when the more powerful GPU is used, with times decreasing from 1,161 to 54 seconds on a desktop computer. Similarly, on the RBNR dataset, the extreme-sized model exhibits a significant reduction in prediction time from 373 to 15 seconds when using the more powerful GPU. In terms of accuracy, we found varying performance across scenarios and datasets. For example, not good enough results are obtained in most scenarios on the TGCRBNW dataset (lower than 50% in all sets and models), while YOLOv8m obtain the high accuracy in several scenarios on the RBNR dataset (almost 80% of accuracy in the best set). Variability in prediction times was observed between different computer architectures, highlighting the importance of selecting appropriate hardware for specific tasks. These results emphasize the importance of aligning computational resources with the demands of real-world tasks to achieve timely and accurate predictions.
[+]

Keywords

5gBib number detectionBroad-bandBroadcastingCognitive networksCorEdge computingExperienceIeee transactionsImage qualitImage qualityMedia streamingObject detectionQualityReal-time systemsRunner segmentationSpecial-issueYolo

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE TRANSACTIONS ON BROADCASTING due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 89/368, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Electrical & Electronic.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-25:

  • WoS: 6
  • Scopus: 7
[+]

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-25:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 10 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/87873/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 161
  • Downloads: 221
[+]

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Martinez, Rafael) and Last Author (JIMENEZ BERMEJO, DAVID).

the author responsible for correspondence tasks has been DEL RIO PONCE, ALBERTO.

[+]

Awards linked to the item

This work was supported in part by the Horizon Europe CODECO Project under Grant 101092696; in part by the Horizon Europe NEMO Project under Grant 101070118; and in part by the UNICO-5G I+DTSI063000-2021-79 (B5GEMINI-AIUC) Project funded by the Ministry of Economic Affairs and Digital Transformation of the Spanish Government and the NextGenerationEU [Recovery, Transformation and Resilience Plan(PRTR)].
[+]