A machine learning-based detection, classification, and quantification of marine litter along the central east coast of India - Raju, Mallela Pruthvi; Veerasingam, Subramanian; Suneel, V.; Asim, Fahad Syed; Khalil, Hana Ahmed; Chatting, Mark; Suneetha, P.; Vethamony, P. (Frontiers in Marine Science, 2025)
Globally, the growth of plastic production has increased exponentially from 1.5 million metric tons (Mt) in 1950 to 400.3 Mt in 2022, resulting in a substantial increase of marine litter along the coastal region. Presently, there is a growing interest in using an artificial intelligence (AI) based automatic and cost-effective approach to identify marine litter for clean-up processes. This study aims to understand the spatial distribution of marine litter along the central east coast of India using the conventional method and AI based object detection approach. From the field survey, a total of 4588 marine litter items could be identified, with an average of 1.147 ± 0.375 items/m2. Based on clean coast index, 37.5% of beaches were categorized as ‘dirty’ and 62.5% of beaches as ‘extremely dirty’. For the machine learning approach ‘You Only Look Once (YOLOv5)’ model was used to detect and classify various types of marine litter items. A total of 9714 images representing seven categories of marine litter (plastic, metal, glass, fabric, paper, processed wood, and rubber) were extracted from eight field videos recorded across diverse beach settings. The efficiency of the trained machine learning model was assessed using different metrices such as Recall, Precision, Mean average precision (mAP) and F1 score (a metric for forecast accuracy). The model achieved a F1 score of 0.797, mAP 0.5 of 0.95, and mAP@0.5-0.95 of 0.76, and these results show that YOLOv5 model could be used in conjunction with conventional marine litter monitoring, classification and detection to provide quick and accurate results.