Autonomous Robotic Surface Inspection Using Computer Vision

  • Authors

    • Dr. Arvind Kumar Singh Professor, Jawaharlal Nehru University, India Author
    • Dr. Lakshmi Narayanan Associate Professor, Bharathidasan University, India Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2022PI4R1P

    Published 01-04-2022

  • Autonomous Robotics, Computer Vision, Surface Inspection, Deep Learning, Industrial Automation, Machine Vision, Defect Detection, Image Processing, Artificial Intelligence, Predictive Maintenance, Industry 4.0, Smart Manufacturing

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    A. K. Singh and L. Narayanan, “Autonomous Robotic Surface Inspection Using Computer Vision”, IJIARE, vol. 5, no. 1, pp. 01–16, Jan. 2022, doi: 10.67228/30715725/IJIARE-2022PI4R1P.

  • Abstract

    Autonomous robotic surface inspection combines intelligent robotics with computer vision to enable accurate, real-time, and contactless detection of surface defects such as cracks, corrosion, scratches, dents, and coating degradation. Unlike manual inspection, it improves consistency, enhances safety, reduces inspection time, and lowers operational costs. By integrating AI, deep learning, edge computing, IIoT, and Digital Twin technologies, autonomous robots can navigate complex industrial environments, capture high-resolution images, and perform automated defect analysis with minimal human intervention. The proposed framework integrates robotic navigation, visual sensing, image processing, defect classification, and maintenance decision support to achieve reliable inspection across diverse industrial sectors. This approach supports predictive maintenance, improves quality assurance, and advances smart manufacturing in Industry 4.0.

  • References

    [1] Y. Zhang, X. Li, and J. Wang, “Deep Learning-Based Surface Defect Detection for Intelligent Manufacturing: A Review,” IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5567–5578, 2021.

    [2] H. Ren, Y. Liu, and Z. Chen, “Autonomous Robotic Inspection Systems for Industrial Applications: Technologies, Challenges, and Future Directions,” IEEE Access, vol. 9, pp. 145210–145226, 2021.

    [3] M. Wang, L. Zhang, and K. Liu, “Computer Vision Techniques for Automated Industrial Inspection: A Comprehensive Survey,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1234–1248, 2022.

    [4] S. K. Saha, P. Kumar, and R. Singh, “Deep Learning-Based Visual Inspection for Manufacturing Defects Using Convolutional Neural Networks,” IEEE Transactions on Industrial Electronics, vol. 69, no. 11, pp. 11842–11852, 2022.

    [5] J. Chen, Z. Huang, and Y. Xu, “Robotic Surface Inspection Using Multimodal Sensors and Artificial Intelligence for Smart Manufacturing,” IEEE Sensors Journal, vol. 22, no. 18, pp. 17540–17551, 2022.

    [6] A. Krizhevsky, I. Sutskever, and G. Hinton, “Advances in Deep Learning Architectures for Industrial Vision Applications,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5321–5336, 2022.

    [7] P. Tang, H. Zhang, and W. Zhao, “YOLO-Based Real-Time Defect Detection for Autonomous Robotic Inspection in Manufacturing Environments,” IEEE Access, vol. 10, pp. 98765–98778, 2022.

    [8] R. Kumar, S. Sharma, and M. Singh, “Digital Twin Enabled Robotic Inspection Framework for Predictive Maintenance in Industry 4.0,” IEEE Transactions on Industrial Informatics, vol. 19, no. 5, pp. 6421–6432, 2023.

    [9] L. Wang, J. Liu, and X. Gao, “Vision Transformer-Based Industrial Surface Defect Detection With Attention Mechanisms,” IEEE Transactions on Image Processing, vol. 32, pp. 4215–4228, 2023.

    [10] N. Patel, A. Gupta, and R. Mehta, “Edge Intelligence for Autonomous Robotic Inspection Using Lightweight Deep Learning Models,” IEEE Internet of Things Journal, vol. 10, no. 12, pp. 10521–10534, 2023.

    [11] F. Zhang, Y. Zhou, and D. Li, “Federated Learning-Based Industrial Defect Detection for Privacy-Preserving Smart Manufacturing,” IEEE Transactions on Industrial Informatics, vol. 20, no. 2, pp. 2315–2327, 2024.

    [12] M. Ali, S. Hussain, and T. Rahman, “Autonomous Mobile Robot Navigation and Intelligent Inspection Using LiDAR and Deep Reinforcement Learning,” IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 845–852, 2024.

    [13] K. Zhao, J. Wu, and P. Chen, “Explainable Artificial Intelligence for Trustworthy Industrial Visual Inspection Systems,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 3, pp. 1120–1133, 2024.

    [14] D. Lee, H. Park, and S. Kim, “Multisensor Fusion-Based Robotic Inspection Framework for Complex Industrial Environments,” IEEE Sensors Journal, vol. 25, no. 1, pp. 215–227, 2025.

    [15] A. Sharma, V. Singh, and R. Kumar, “Next-Generation Autonomous Robotic Inspection Systems: Integrating AI, Edge Computing, and Digital Twins for Industry 5.0,” IEEE Access, vol. 14, pp. 33450–33468, 2026.

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