Authors : Ravi Bagade; Kavita Killiketar

Volume/Issue : Volume 9 - 2024, Issue 9 - September

Google Scholar : https://tinyurl.com/4f38djyf

Scribd : https://tinyurl.com/3m5mbems

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP1488

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Indoor localization systems have gained significant attention in recent years due to their applications in various fields such as smart homes, retail environments, and healthcare facilities. This paper presents an innovative approach to indoor localization through the integration of object detection techniques, aiming to enhance accuracy and efficiency in identifying and locating objects within indoor spaces. We explore the use of advanced deep learning algorithms, particularly convolutional neural networks (CNNs), to detect and classify objects in real-time. Our methodology involves collecting a comprehensive dataset of indoor environments, training a robust object detection model, and implementing it in a localization framework that utilizes both visual and spatial data. The experimental results demonstrate that our proposed system achieves high detection accuracy and reduced localization errors, outperforming traditional methods. Furthermore, we discuss the potential of leveraging object recognition to improve user experience and navigation in complex indoor settings. This research contributes to the evolving field of indoor localization and offers a foundation for future developments in intelligent indoor navigation systems.

Keywords : Indoor Localization, Object Detection, Convolutional Neural Networks (CNNs).

References :

1. Beeta Narayan, B. Noble, and Aiswariya Binu, “Survey paper on indoor object detection and voice feedback system”, International Research Journal of Engineering and Technology (IRJET), Volume: 11 Issue: 04 | Apr 2024
2. Hiren Kumar Thakkar and Suresh Merugu, “Object Detection System for Visually Impaired Persons Using Smartphone,” in ResearchGate , January 2022.
3. Tasnia Ashrafi Heya,Sayed Erfan Arefin,"Image Processing Based Indoor Localization System for Assisting Visually Impaired People",IEEE,2018.
4. Joseph Redmon, Santosh Divvala, “You Only Look Once: Unified, Real-Time Object Detection”, IEEE Conference on Computer Vision and Pattern Recognition, 2016.
5. Mark Sandler, Andrew Howard, “MobileNetV2: Inverted Residuals and Linear Bottlenecks”, Conference on Computer Vision and Pattern Recognition, 2018.