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.
Published Version
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