Next Article in Journal
LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving
Previous Article in Journal
WeatherClean: An Image Restoration Algorithm for UAV-Based Railway Inspection in Adverse Weather
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Correction

Correction: Santos et al. Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection. Sensors 2024, 24, 4219

by
David O. Santos
1,2,
Jugurta Montalvão
2,3,
Charles A. C. Araujo
4,
Ulisses D. E. S. Lebre
4,
Tarso V. Ferreira
2,3,* and
Eduardo O. Freire
2,3,5
1
Department of Electrical Engineering, Federal University of Campina Grande, Campina Grande 58401-490, Brazil
2
INESC P&D Brasil, Santos 11055-300, Brazil
3
Department of Electrical Engineering, Federal University of Sergipe, São Cristóvão 49100-000, Brazil
4
Electrical Operation, Eneva S.A., Barra dos Coqueiros 49140-000, Brazil
5
National Council of Scientific and Technical Research—CONICET, Godoy Cruz, Buenos Aires 2290, Argentina
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(15), 4801; https://doi.org/10.3390/s25154801
Submission received: 18 March 2025 / Accepted: 3 July 2025 / Published: 4 August 2025
(This article belongs to the Topic Applications in Image Analysis and Pattern Recognition)
The changes are described below:
  • INESC P&D Brasil was included as an entity to which several authors are affiliated;
  • David O. Santos, Jugurta Montalvão, Tarso V. Ferreira, and Eduardo O. Freire were linked to INESC P&D Brasil;
  • The author to whom correspondence should be sent was changed to Tarso V. Ferreira <tarso.vilela@inescbrasil.org.br>.
Despite these difficulties, there have already been remarkable achievements in object detection [1].
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Santos, D.O.; Montalvão, J.; Araujo, C.A.C.; Lebre, U.D.E.S.; Ferreira, T.V.; Freire, E.O. Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection. Sensors 2024, 24, 4219. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Santos, D.O.; Montalvão, J.; Araujo, C.A.C.; Lebre, U.D.E.S.; Ferreira, T.V.; Freire, E.O. Correction: Santos et al. Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection. Sensors 2024, 24, 4219. Sensors 2025, 25, 4801. https://doi.org/10.3390/s25154801

AMA Style

Santos DO, Montalvão J, Araujo CAC, Lebre UDES, Ferreira TV, Freire EO. Correction: Santos et al. Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection. Sensors 2024, 24, 4219. Sensors. 2025; 25(15):4801. https://doi.org/10.3390/s25154801

Chicago/Turabian Style

Santos, David O., Jugurta Montalvão, Charles A. C. Araujo, Ulisses D. E. S. Lebre, Tarso V. Ferreira, and Eduardo O. Freire. 2025. "Correction: Santos et al. Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection. Sensors 2024, 24, 4219" Sensors 25, no. 15: 4801. https://doi.org/10.3390/s25154801

APA Style

Santos, D. O., Montalvão, J., Araujo, C. A. C., Lebre, U. D. E. S., Ferreira, T. V., & Freire, E. O. (2025). Correction: Santos et al. Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection. Sensors 2024, 24, 4219. Sensors, 25(15), 4801. https://doi.org/10.3390/s25154801

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop