{rfName}
SS

Indexed in

License and use

Icono OpenAccess

Citations

111

Altmetrics

Grant support

This research was funded by the Spanish Ministry of Science and Innovation, under the RETOS Programme, grant number: RTI2018-098019-B-I00 and project TIN2017-89723-P; by the CYTED Network Ibero-American Thematic Network on ICT Applications for Smart Cities, grant number: 518RT0559; and by the CERCA Programme/Generalitat de Catalunya.

Analysis of institutional authors

Sanchez, NCorresponding AuthorMoreno, AbAuthorVelez, JfAuthor

Share

Publications
>
Article

SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities

Publicated to:Sensors. 20 (16): 1-23 - 2020-08-01 20(16), DOI: 10.3390/s20164587

Authors: Morera, Angel; Sanchez, Angel; Belen Moreno, A; Sappa, Angel D; Velez, Jose F

Affiliations

Abstract

This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images offers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the difficulty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with different types of semantic segmentation networks and using the same evaluation metrics is also included.

Keywords

Complex backgroundDeep neural networksDetection metricsDetection problemsEvaluation metricsFalse positiveIllumination conditionsMultiple variabilityObject and scene imaging variabilitiesObject detectionOne-stage detectorsPartial occlusionsSemantic segmentationSemanticsSingle shot multibox detector (ssd)Text detectionUrban outdoor panelsYou only look once (yolo)

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Sensors 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, 2020, it was in position 14/64, thus managing to position itself as a Q1 (Primer Cuartil), in the category Instruments & Instrumentation.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.98. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.64 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 18.66 (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-07, the following number of citations:

  • WoS: 38
  • Scopus: 64
  • Europe PMC: 9
  • OpenCitations: 54

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 2025-06-07:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 117.
  • 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: 116 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

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.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Ecuador.

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 () and Last Author (Vélez Serrano, José Francisco).

the author responsible for correspondence tasks has been Sánchez Calle, Ángel.