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All Journal IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI)
Messbah, Hind
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Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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Object detection for indoor mobile robot: deep learning approaches review Messbah, Hind; Emharraf, Mohamed; Saber, Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3520-3527

Abstract

Efficient object detection is crucial for enabling autonomous indoor robot navigation. This paper reviews current methodologies and challenges in the field, with a focus on deep learning-based techniques. Methods like you only look once (YOLO), region-based convolutional neural networks (R-CNN), and Faster R-CNN are explored for their suitability in real-time detection in dynamic indoor environments. Deep learning models are emphasized for their ability to improve detection accuracy and adaptability to varying conditions. Key performance metrics such as accuracy, speed, and scalability across different object types and environmental scenarios are discussed. Additionally, the integration of object detection with navigation systems is examined, highlighting the importance of accurate perception for safe and effective robot movement. This study provides insights into future research directions aimed at advancing the capabilities of indoor robot navigation through enhanced deep learning-based object detection techniques.
Co-Authors Emharraf, Mohamed Saber, Mohamed Saber, Mohammed