Development of Relevance Propagation Rule-based Systems and Faster Mask R-CNN for IoT-Enabled Surveillance Drones to Enhance Autonomous Navigation and Collision Avoidance
| International Journal of Electronics and Communication Engineering |
| © 2025 by SSRG - IJECE Journal |
| Volume 12 Issue 5 |
| Year of Publication : 2025 |
| Authors : L. Ganesh Babu, V. Balambica, M. Achudhan |
10.14445/23488549/IJECE-V12I5P104
How to Cite?
L. Ganesh Babu, V. Balambica, M. Achudhan, "Development of Relevance Propagation Rule-based Systems and Faster Mask R-CNN for IoT-Enabled Surveillance Drones to Enhance Autonomous Navigation and Collision Avoidance," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 5, pp. 33-49, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I5P104
Abstract:
The study focuses on enhancing autonomous navigation and improving collision avoidance by developing a Relevance Propagation Rule-Based on Faster Mask R-CNN (RPR-FMRCNN) leveraging IoT-enabled surveillance drones to enhance smart decision-making. Autonomous drones are increasingly being used for various tasks, but operating them safely and efficiently in complex environments remains a significant challenge, particularly in avoiding collisions with moving obstacles. Existing approaches struggle with real-time decision-making and are often imprecise and unstable in rapidly changing scenarios. The paper proposes a hybrid system for obstacle recognition to address these issues. IoT is used to gather real-time information, ensuring immediate updates. The rule-based approach prioritizes obstacle relevance to dynamically create safer paths, while FMRCNN accurately identifies obstacles, providing boundaries and segmentation masks for each object. IoT-enabled surveillance drones offer seamless connectivity and data exchange, facilitating continuous environmental updates and informed decision-making. The primary goal is to develop a system capable of autonomously determining optimal navigation routes, accurately identifying and categorizing obstacles, and making well-informed decisions to avoid them. By combining deep neural networks with IoT, the model aims to provide real-time processing with improved precision and efficiency. The study's results show significant improvements in obstacle recognition and navigation, with the system performing better in dynamic environments. The outcomes demonstrate a substantial reduction in crashes, enhancing the overall reliability of drones. Compared to existing methods, the proposed model improves collision avoidance efficiency by 15% and navigation accuracy by 12%, signaling promising advancements in autonomous drone systems for various applications.
Keywords:
Relevance Propagation Rule-Based Systems, Faster Mask R-CNN, IoT-Enabled surveillance drones, Autonomous navigation, Collision avoidance, Intelligent Decision-Making, Obstacle detection, Real-Time data processing, Deep learning, Drone navigation.
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