Enhancing Marine Conservation: YOLOv8-based Underwater Waste Detection System

Robust machine-learning algorithms are essential for successfully operating underwater directions and intelligent object recognition systems in turbid water. The development of modern civilization has resulted in increased pollution caused by humans in marine environments, specifically oceans, rivers, and lakes, contaminating our valuable water resources. Despite environmental regulations, solid waste in the form of waste, rubbish, and garbage continues to be disposed of directly into the ocean, adversely impacting the survival and well-being of underwater creatures. Therefore, it is crucial to have an appropriate technique for accurately detecting and analyzing the features present in these particular environments. In this research paper, we have implemented underwater waste detection using the YoloV8 algorithm, and the dataset consists of 5127 images belonging to a variety of classes are included in the collection. These classes include masks, metal tins or cans, glass bottles, gloves, plastic bags, tires, etc. The images were taken in a variety of diverse underwater environments. This research also addresses the issue of growing underwater waste in oceans and seas by detecting wastes in underwater images.