Towards Underwater Sustainability with AIoT: Underwater Trash Management System Comprised of YOLOv8 with IoT-Applied Segmentation

摘要

Effective techniques for managing rubbish underwater are essential due to growing environmental concerns about contamination under the sea. In this research, we offer a novel method for efficient underwater trash detection and management that makes use of the Internet of Things (IoT) and the YOLOV8 object detection model. Using YOLOV8, the suggested system seeks to identify and categorize underwater garbage in real-time. It then uses IoT devices for segmentation and management. We outline the suggested system's design, talk about how YOLOV8 integrates with Internet of Things gadgets, and provide experimental results to validate the system's efficacy. Our findings suggest that the suggested strategy has great potential to advance efforts to manage undersea waste and manage pollution as well as segment using 6-DOF Robotic Arm. The IoT based system allows remote monitoring of trash density and location along with the raspberry-pi server-based video feed allows the users to suitably monitor aquatic conditions remotely. The fusion of IoT and deep learning on YOLO v8 model opens gateway for water sustainability and enhances further opportunities in employing robotic arm-based trash collection system.