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Leveraging AI and Semantic Web Technologies for Enhanced Image Processing in Robotic Systems

Author

Listed:
  • Hao Li

    (Hunan Biological and Electromechanical Polytechnic, China)

  • Yedong Chen

    (Hunan Biological and Electromechanical Polytechnic, China)

  • Ziming Xie

    (Hunan University of Technology, China)

  • Bo Ye

    (Hunan Biological and Electromechanical Polytechnic, China)

  • Shavi Bansal

    (Insights2Techinfo, India & & UCRD, Chandigarh University, Chandigarh, India)

Abstract

Fall detection is an important task in the robotic system, and it is related to the well-being of the human subject. However, it is a challenging task to complete due to the presence of false positives and a lack of contextual awareness. In this context, this paper presents a fall detection model that integrates You Only Look Once11—for real-time object detection—with semantic web technologies for intelligent decision-making. The semantic knowledge graph is used to establish a relation between the You Only Look Once11 results and the fall events. Experimental evaluation of the proposed model presents an mAP@0.5 of 0.806, validating the effectiveness of artificial intelligence-driven visual processing combined with semantic knowledge representation for enhanced fall detection in robotic systems.

Suggested Citation

  • Hao Li & Yedong Chen & Ziming Xie & Bo Ye & Shavi Bansal, 2025. "Leveraging AI and Semantic Web Technologies for Enhanced Image Processing in Robotic Systems," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 21(1), pages 1-19, January.
    • Handle: RePEc:igg:jswis0:v:21:y:2025:i:1:p:1-19
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