Detection and Classification of Floating Plastic Litter Using a Vessel-Mounted Video Camera and Deep Learning

Abstract

Marine plastic pollution is a major environmental concern, with significant ecological, economic, public health and aesthetic consequences. Despite this, the quantity and distribution of marine plastics is poorly understood. Better understanding of the global abundance and distribution of marine plastic debris is vital for global mitigation and policy. Remote sensing methods could provide substantial data to overcome this issue. However, developments have been hampered by the limited availability of in situ data, which are necessary for development and validation of remote sensing methods. Current in situ methods of floating macroplastics (size greater than 1 cm) are usually conducted through human visual surveys, often being costly, time-intensive and limited in coverage. To overcome this issue, we present a novel approach to collecting in situ data using a trained object-detection algorithm to detect and quantify marine macroplastics from video footage taken from vessel-mounted general consumer cameras. Our model was able to successfully detect the presence or absence of plastics from real-world footage with an accuracy of 95.2% without the need to pre-screen the images for horizon or other landscape features, making it highly portable to other environmental conditions. Additionally, the model was able to differentiate between plastic object types with a Mean Average Precision of 68% and an F1-Score of 0.64. Further analysis suggests that a way to improve the separation among object types using only object detection might be through increasing the proportion of the image area covered by the plastic object. Overall, these results demonstrate how low-cost vessel-mounted cameras combined with machine learning have the potential to provide substantial harmonised in situ data of global macroplastic abundance and distribution.

Progress in Research on Marine Litter-Related Monitoring Technologies

“…The correlation between the camera-identified large pieces of litter (>50 cm) and the trawl results was high, and the correlation with the trawl results for small pieces of litter (<5 cm) was directly proportional to the size of the litter. Similarly, Armitage et al [48] used the same algorithm to monitor the drifting litter in the south-western bays of the United Kingdom, and the results showed that the accuracy of the identification of the plastic litter was as high as 95.2%, and the accuracy of the classification of the plastic litter was 68%.…”

Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor

“…Video cameras and images have also been used for the detection and recognition of plastics based on the objects' shape and colour [18][19][20][21]. Colour images were used together with SVM and YOLOv5 networks for detection and classification of plastics with accuracies of 94.7% and 95.2%, respectively [22,23]. These methods showed to be accurate; however, they focused mainly on the recognition of plastic bottles.…”

Study of Beach Litter on Remote Island, Case Study: Ainoshima Island, Japan

“…The Plastic (Polystyrene) category is the first category most found on the sandy beaches of Ainoshima Island. Since the mass production of consumer plastic products began in the 1950s (Armitage et al, 2022), plastic waste has reached the natural environment, accounting for 60-80% of marine debris globally.…”