Using Computer Vision to Clean the World's Oceans

Global plastic production has surpassed 500 million tons, with 30% potentially reaching the oceans, prompting researchers from CSU Monterey Bay, The Ocean Cleanup, and UC San Diego to explore how computer vision can aid in ocean cleanup. Their study, DeepPlastic, evaluates the effectiveness of autonomous underwater vehicles (AUVs) equipped with deep learning models in identifying and collecting underwater plastics. The researchers compiled a dataset from real-world conditions using images from California sites and the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). They tested model architectures such as YOLOv4, YOLOv4-tiny, and YOLOv5, with YOLOv5 showing a promising balance of accuracy and inference speed. Despite achieving a 0.98 mAP and a throughput of 1.4 ms per image on a Tesla V100, the model still requires improvement, as misclassification is possible, necessitating further data collection and model iteration. The research highlights the potential of computer vision applications in enhancing environmental health and encourages continued innovation in this field.