International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Sea-Sentry: A Cost-Effective Modular ROV for Underwater Surveillance and Infrastructure Inspection |
|---|---|
| ABSTRACT | Exploring underwater environments is crucial for maintaining biodiversity and industrial infrastructure, yet it remains a difficult and often dangerous task. Traditional methods, like sending human divers for inspections, are not only risky but also incredibly expensive. To address this, we developed "Sea-Sentry," an affordable and modular Remotely Operated Vehicle (ROV) designed to make underwater monitoring accessible. This system is engineered to handle critical tasks such as real-time video surveillance, pollution tracking, and pipeline inspection. By integrating intelligent features like the "Deep Sense Shield" for avoiding obstacles and "Pipe Guard Fusion" for detecting structural cracks, Sea-Sentry offers a robust solution for marine research. Powered by open-source technologies including the ESP32-S3 and YOLO algorithms, this project demonstrates that high-performance underwater robotics can be both scalable and cost-effective. |
| AUTHOR | KISHAN B, DARSHAN, KISHORE KUMAR K, SURESH N, DR. K. RAMESHA UG Students, Dept. of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, India Professor, Dept. of Electronics and Instrumentation Engineering, Dr. Ambedkar Institute of Technology, Bengaluru, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312148 |
| pdf/148_Sea-Sentry A Cost-Effective Modular ROV for Underwater Surveillance and Infrastructure Inspection.pdf | |
| KEYWORDS | |
| References | [1] R. Mandal, R. M. Connolly, T. A. Schlacher, and B. Stantic, "Assessing Fish Abundance from Underwater Video Using Deep Neural Networks," arXiv preprint arXiv:1807.05838, 2018. [2] Y. M. Ahmed, O. Yaakob, and B. K. Sun, "Design of a New Low Cost ROV Vehicle," Jurnal Teknologi, vol. 69, no. 7, pp. 27-31, 2014. [3] J. Yang, X. Cai, Boshen Liu, Fei Ma, and Qian Jiao, "Target Recognition Using Rotating Ultrasonic Sensor for an Amphibious ROV," Measurement Science and Technology, vol. 34, no. 5, 2023. [4] D. B. Duraibabu et al., "An Optical Fibre Depth (Pressure) Sensor for Remote Operated Vehicles in Underwater Applications," Sensors, vol. 17, no. 2, p. 406, 2017. [5] Zhihao Wang et al., "Investigation of Submerged MEMS Ultrasonic Sensors for Underwater Obstacle Avoidance Application," Remote Sensing, vol. 16, no. 3, p. 497, 2024. [6] Q. Jing, J. Luo, and Y. Li, "A New Modular Intensive Design Solution for ROVs," in Advances in Mechanism and Machine Science, Springer, Cham, 2021, pp. 317-326. [7] H. El-Messiry et al., "Real-Time Crack Detection Using ROV," in Intelligent Computing, Springer, Cham, 2021, pp. 922-929. [8] J. Huang, C. Fang, X. Zheng, and J. Liu, "YOLOv8-UC: An Improved YOLOv8-Based Underwater Object Detection Algorithm," IEEE Access, vol. 12, 2024. |