IOT – Based Under Water Object Detection

An IoT-based real-time underwater object detection system is developed to identify marine debris, aquatic species, and submerged structures by integrating edge computing with machine learning. A waterproof camera coupled with a Raspberry Pi captures live underwater footage, processed locally using OpenCV and YOLO algorithms for efficient and low-latency object recognition. The detected information is transmitted via wireless IoT protocols such as Wi-Fi or LoRa for remote monitoring and analysis. The system is cost-effective, scalable, and adaptable to various underwater conditions, enabling continuous data acquisition for marine research, pollution detection, and environmental monitoring. Its modular design supports additional sensors like temperature and pH modules for enhanced ecological insight. Future developments aim to incorporate advanced deep learning and computer vision models to further improve detection accuracy and robustness under challenging underwater conditions, contributing to smart and sustainable marine observation.