Hybrid Deep Learning Frameworks for Robotic Object Recognition

  • Authors

    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India Author
    • H. N. Mahabala Computer Scientist, Tata Institute of Fundamental Research, India Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2025PI4T1I

    Published 03-05-2025

  • Robotic Object Recognition, Hybrid Deep Learning, Computer Vision, Convolutional Neural Networks, Vision Transformers, Sensor Fusion, Intelligent Robotics, Autonomous Systems, Object Detection, Artificial Intelligence

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    N. Seshagiri and H. N. Mahabala, “Hybrid Deep Learning Frameworks for Robotic Object Recognition”, IJIARE, vol. 8, no. 1, pp. 01–17, Mar. 2025, doi: 10.67228/30715725/IJIARE-2025PI4T1I.

  • Abstract

    Robotic object recognition is a fundamental capability that enables autonomous robots to interact intelligently with dynamic environments. Traditional vision-based methods, such as SIFT, SURF, HOG, and template matching, perform well under controlled conditions but struggle with variations in lighting, viewpoint, occlusion, and complex backgrounds. Recent advances in deep learning have significantly improved recognition accuracy by automatically learning features from raw image data. However, individual deep learning models often face challenges related to computational cost, inference speed, and limited generalization. This paper proposes a Hybrid Deep Learning Framework that integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), attention mechanisms, and multimodal sensor fusion (RGB, depth, and LiDAR) to enhance recognition accuracy and efficiency. The framework combines local and global feature extraction, adaptive feature fusion, intelligent object recognition, and robotic decision-making for real-time perception and task execution. It supports applications in industrial automation, warehouse logistics, autonomous mobile robots, healthcare, agriculture, and service robotics while improving robustness, scalability, and computational efficiency.

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