Beach Litter Detection in Coastal Environments Using High Resolution Images

Marine debris, such as discarded fishing gear, plastic waste, and chemical pollutants, contributes to beach litter and posesa major threat to coastal ecosystems, with increasing coastal populations worsening pollution and its harmful effects on marine life. Effectively addressing the issue is challenging because to the slowness and labor-intensiveness of traditional litter detection techniques, such as manual surveys. High-resolution photos and deep learning technology-more especially, the YOLO (You Only Look Once) model-are used in this study to suggest a solution. employing high-resolution photos, which the YOLO algorithm analyzes to effectively and accurately detect and categorize litter. Because of its single-pass structure, YOLO operates more quickly than more traditional techniques like CNN and R-CNN, which makes it perfect for real-time applications. This approach helps to prevent further deterioration of marine ecosystems by reducing the need for manual monitoring, saving time, and more accurately identifying pollution hotspots. This invention uses cutting-edge technology to provide a quick, effective, and flexible solution to the growing problem of marine litter globally.