Detection and classification of floating plastic litter using a vessel-mounted video camera and deep learning
Armitage, S.; Awty-Carroll, K.; Clewley, D.; Martinez-Vicente, V. (2022). Detection and classification of floating plastic litter using a vessel-mounted video camera and deep learning. Remote Sens. 14(14): 3425. https://dx.doi.org/10.3390/rs14143425 In: Remote Sensing. MDPI: Basel. ISSN 2072-4292; e-ISSN 2072-4292, more |   |
| Keyword | | | Author keywords | artificial intelligence; machine learning; object detection; floating plastic litter; plastic pollution; macroplastics; remote sensing; monitoring; vessel-based |
| Authors | | Top | - Armitage, S.
- Awty-Carroll, K.
- Clewley, D.
- Martinez-Vicente, V., more
| | |
| 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. |
|
