Department of Earth and Environmental Sciences, University of Milan-Bicocca, Milan, Italy; MaRHE Center (Marine Research and High Education Center), Magoodhoo Island Faafu Atoll, Maldives.
DeepTrace Technologies S.R.L., Milan, Italy.
Sci Total Environ. 2019 Nov 25;693:133581. doi: 10.1016/j.scitotenv.2019.133581. Epub 2019 Jul 24.
Anthropogenic Marine Debris (AMD) is one of the major environmental issues of our planet to date, and plastic accounts for 80% of total AMD. Beaches represent one of the main marine compartment where AMD accumulates, but few and scattered regional assessments are available from literature reporting quantitative estimation of AMD distributed on the shorelines. However, accessing information on the AMD accumulation rate on beaches, and the associated spatiotemporal oscillations, would be crucial to refining global estimation on the dispersal mechanisms. In our work, we address this issue by proposing an ad-hoc methodology for monitoring and automatically quantifying AMD, based on the combined use of a commercial Unmanned Aerial Vehicle (UAV) (equipped with an RGB high-resolution camera) and a deep-learning based software (i.e.: PlasticFinder). Remote areas were monitored by UAV and were inspected by operators on the ground to check and to categorise all AMD dispersed on the beach. The high-resolution images obtained from UAV allowed to visually detect a percentage of the objects on the shores higher than 87.8%, thus providing suitable images to populate training and testing datasets, as well as gold standards to evaluate the software performance. PlasticFinder reached a Sensitivity of 67%, with a Positive Predictive Value of 94%, in the automatic detection of AMD, but a limitation was found, due to reduced sunlight conditions, thus restricting to the use of the software in its present version. We, therefore, confirmed the efficiency of commercial UAVs as tools for AMD monitoring and demonstrated - for the first time - the potential of deep learning for the automatic detection and quantification of AMD.
人为海洋垃圾 (AMD) 是迄今为止地球面临的主要环境问题之一，其中塑料占 AMD 的 80%。海滩是 AMD 积累的主要海洋区域之一，但从文献中可获得的关于 AMD 分布在海岸线的定量评估却很少且分散。然而，获取海滩上 AMD 积累率的信息，以及相关的时空波动情况，对于完善全球对分散机制的估计将至关重要。在我们的工作中，我们通过提出一种专门的监测和自动量化 AMD 的方法来解决这个问题，该方法基于商用无人机 (UAV)（配备 RGB 高分辨率相机）和基于深度学习的软件（即：PlasticFinder）的组合使用。操作人员在地面上检查和分类海滩上所有分散的 AMD，以监测远程区域的 UAV。从 UAV 获得的高分辨率图像可以直观地检测到海岸上物体的比例高于 87.8%，从而提供了适合的图像来填充训练和测试数据集，以及评估软件性能的黄金标准。PlasticFinder 在 AMD 的自动检测中达到了 67%的灵敏度和 94%的阳性预测值，但由于阳光条件的减少，发现了一个限制，从而限制了该软件在其现有版本中的使用。因此，我们证实了商用无人机作为 AMD 监测工具的效率，并首次展示了深度学习在 AMD 的自动检测和量化中的潜力。
