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Grant support

This research has been possible with the support of the Secretariad' Universitatsi Recercadel Departamentd' Empresai Coneixement de la Generalitat de Catalunya (2020 FISDU 00405). We are thankfully acknowledging the use of the University of Rovira I Virgili (URV) facilities to carry out this work.

Analysis of institutional authors

Cristiano Rodríguez, Julián EfrénAuthorMasoumian, ArminCorresponding AuthorAbdulwahab, SaddamAuthorCristiano, JuliánAuthorPuig, DomenecAuthorRashwan, Hatem AAuthor
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Proceedings Paper

Absolute Distance Prediction Based on Deep Learning Object Detection and Monocular Depth Estimation Models

Publicated to:Frontiers In Artificial Intelligence And Applications. 339 325-334 - 2021-01-01 339(), DOI: 10.3233/FAIA210151

Authors: Masoumian, Armin; Marei, David G F; Abdulwahab, Saddam; Cristiano, Julian; Puig, Domenec; Rashwan, Hatem A

Affiliations

Rovira & Virgili Univ, DEIM, Tarragona 43007, Spain - Author

Abstract

Determining the distance between the objects in a scene and the camera sensor from 2D images is feasible by estimating depth images using stereo cameras or 3D cameras. The outcome of depth estimation is relative distances that can be used to calculate absolute distances to be applicable in reality. However, distance estimation is very challenging using 2D monocular cameras. This paper presents a deep learning framework that consists of two deep networks for depth estimation and object detection using a single image. Firstly, objects in the scene are detected and localized using the You Only Look Once (YOLOv5) network. In parallel, the estimated depth image is computed using a deep autoencoder network to detect the relative distances. The proposed object detection based YOLO was trained using a supervised learning technique, in turn, the network of depth estimation was self-supervised training. The presented distance estimation framework was evaluated on real images of outdoor scenes. The achieved results show that the proposed framework is promising and it yields an accuracy of 96% with RMSE of 0.203 of the correct absolute distance.

Keywords
Deep learningDepth estimationDistance predictioDistance predictionObject detection

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Frontiers In Artificial Intelligence And Applications, Q4 Agency Scopus (SJR), its regional focus and specialization in Artificial Intelligence, give it significant recognition in a specific niche of scientific knowledge at an international level.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 1.4, which 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:

  • Field Citation Ratio (FCR) from Dimensions: 8.17 (source consulted: Dimensions May 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-05-20, the following number of citations:

  • WoS: 4
  • Scopus: 20
  • Google Scholar: 35
  • OpenCitations: 18
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 2025-05-20:

  • 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: 65.
  • 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: 65 (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: 2.25.
  • The number of mentions on the social network X (formerly Twitter): 5 (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:

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 (Masoumian, Armin) and Last Author (Abdellatif Fatahallah Ibrahim Mahmoud, Hatem).

the author responsible for correspondence tasks has been Masoumian, Armin.