Underwater Waste Detection with YOLO: A High-Performance Computing Technique

Waste accumulation in the oceans poses severe environmental challenges that require efficient mechanisms for detection and removal. The methods usually employ slow and resource-intensive techniques like sonar-based detection and manual inspection, hence the need for automated mechanisms of detection. Waste detection under water is a regular monitoring task. Thus, the work proposes rapid model training which allows real-time deployment and enhancement for prevention of degradation of water bodies for various type of wastes. The work uses high-performance computing techniques such as parallel processing, NVIDIA GPUs, CUDA, and YOLO. A deep learning model featuring Convolutional Neural Networks (CNN) is integrated with YOLO (You Only Look Once) to augment detection speed along with accuracy. The model training time is particularly important for real-time applications which allows rapid iteration for the researchers and practitioners. This work points to the fact that the GPU architecture has accelerated 41X in training deep models and improved over precision, and inference time. In all aspects the proposed model claims to perform real-time waste detection within permissible computational latency. This work is in demand for autonomous systems like underwater drones which demand the fast and timely decision-making capability to operate and maneuver properly in dynamic operational environments.