Detection and assessment of marine litter in an uninhabited island, Arabian Gulf: A case study with conventional and machine learning approaches

Abstract

In 2018, the Ministry of Municipality and Environment, Qatar removed 90 t of marine litter (ML) from the Ras Rakan Island (RRI), a remote uninhabited island in the Arabian Gulf (hereinafter referred to as Gulf). To identify the sources of ML and understand the post-cleaning ML accumulation rate, a ML survey was conducted around RRI in 2019. A total of 1341 ML items were found around RRI with an average abundance of 3.4 items/m². In addition, a machine learning approach was applied to extract the quantity and types of ML from 10,400 images from the sampling sites (beaches) to make the ML clean-up process and monitoring effort more efficient. The image coordinates of ML objects were used to train an object detection algorithm 'You Only Look Once (YOLO-v5)' to automatically detect ML from video data. An image enhancement technique was performed to improve the quality of unclear images. The best performing YOLO-v5 model had 90% of mean Average Precision (mAP) while maintaining near real-time processing speeds at 2 ms/image. The abundance of ML around RRI was higher than that found on the coast of mainland Qatar. 61.5% of the sampling locations are considered as 'extremely dirty' based on Clean Coast Index. Windward beaches had higher ML concentrations (derived from neighbouring countries) than the leeward beaches. Like RRI, most of the uninhabited islands in the Arabian Gulf are home to many seabirds and sea turtles, and could act as major sinks for ML deposition. Therefore, implementation of this machine learning technique to all islands allows estimating and mitigating the load of ML for achieving a sustaining and a cleaner ocean.

Keywords: Machine learning; Marine litter; Plastics; Ras Rakan Island of Qatar; YOLO-v5.