Artificial Intelligence for Ocean Conservation: Sustainable Computer Vision Techniques in Marine Debris Detection and Classification

Marine debris poses a significant threat to the marine ecosystem, and its detection and removal are crucial for environmental sustainability. This study presents a comprehensive study on the application of computer vision techniques for marine debris detection, with a specific focus on the task of trash detection. The study utilizes a diverse and carefully curated dataset named “YOLOv5 Marine Debris” (or any suitable name for the YOLOv5 dataset), obtained from the Ultralytics open-source research repository for YOLOv5 models. Through the utilization of computer vision techniques, this study strives to contribute to the development of sustainable solutions for marine debris detection. The YOLOv5 model’s state-of-the-art capabilities, complemented by the diverse and challenging “YOLOv5 Marine Debris” dataset, enable accurate and reliable trash detection across various underwater environments. By pushing the boundaries of object detection, image processing, and machine learning, this research envisions a future where autonomous robot platforms efficiently monitor and remove marine debris, safeguarding the marine ecosystem.