Indoor Signs Detection for Visually Impaired People: Navigation Assistance Based on a Lightweight Anchor-Free Object Detector.
| Autor: | Said Y; Remote Sensing Unit, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia.; King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia.; Laboratory of Electronics and Microelectronics (LR99ES30), University of Monastir, Monatir 5019, Tunisia., Atri M; College of Computer Sciences, King Khalid University, Abha 62529, Saudi Arabia., Albahar MA; School of Computer Science, Umm Al-Qura University, Mecca 24382, Saudi Arabia., Ben Atitallah A; Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia., Alsariera YA; College of Science, Northern Border University, Arar 91431, Saudi Arabia. |
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| Jazyk: | angličtina |
| Zdroj: | International journal of environmental research and public health [Int J Environ Res Public Health] 2023 Mar 12; Vol. 20 (6). Date of Electronic Publication: 2023 Mar 12. |
| DOI: | 10.3390/ijerph20065011 |
| Abstrakt: | Facilitating the navigation of visually impaired people in indoor environments requires detecting indicating signs and informing them. In this paper, we proposed an indoor sign detection based on a lightweight anchor-free object detection model called FAM-centerNet. The baseline model of this work is the centerNet, which is an anchor-free object detection model with high performance and low computation complexity. A Foreground Attention Module (FAM) was introduced to extract target objects in real scenes with complex backgrounds. This module segments the foreground to extract relevant features of the target object using midground proposal and boxes-induced segmentation. In addition, the foreground module provides scale information to improve the regression performance. Extensive experiments on two datasets prove the efficiency of the proposed model for detecting general objects and custom indoor signs. The Pascal VOC dataset was used to test the performance of the proposed model for detecting general objects, and a custom dataset was used for evaluating the performance in detecting indoor signs. The reported results have proved the efficiency of the proposed FAM in enhancing the performance of the baseline model. |
| Databáze: | MEDLINE |
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