Repositori institucional URV
| URV's Author/s: | Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdulwahab, Saddam Abdulrhman Hamed / Cristiano Rodríguez, Julián Efrén / Masoumian, Armin / Puig Valls, Domènec Savi |
| Author, as appears in the article.: | Masoumian, Armin; Marei, David G F; Abdulwahab, Saddam; Cristiano, Julian; Puig, Domenec; Rashwan, Hatem A |
| Author's mail: | domenec.puig@urv.cat saddam.abdulwahab@urv.cat armin.masoumian@estudiants.urv.cat armin.masoumian@estudiants.urv.cat hatem.abdellatif@urv.cat julianefren.cristianor@urv.cat saddam.abdulwahab@urv.cat |
| Author identifier: | 0000-0002-0562-4205 0000-0001-5421-1637 |
| Journal publication year: | 2021 |
| Publication Type: | Proceedings Paper |
| APA: | Masoumian, Armin; Marei, David G F; Abdulwahab, Saddam; Cristiano, Julian; Puig, Domenec; Rashwan, Hatem A (2021). Absolute Distance Prediction Based on Deep Learning Object Detection and Monocular Depth Estimation Models. Amsterdam: IOS Press |
| Papper original source: | Frontiers In Artificial Intelligence And Applications. 339 325-334 |
| 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. |
| Article's DOI: | 10.3233/FAIA210151 |
| Link to the original source: | https://ebooks.iospress.nl/doi/10.3233/FAIA210151 |
| Papper version: | info:eu-repo/semantics/publishedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Enginyeria Informàtica i Matemàtiques |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii |
| Keywords: | Deep learning Depth estimation Distance predictio Distance prediction Object detection |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2024-09-21 |
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