Politikos Dimitris V, Fakiris Elias, Davvetas Athanasios, Klampanos Iraklis A, Papatheodorou George
Institute of Marine Biological Resources and Inland, Hellenic Centre for Marine Research, 16452 Argyroupoli, Greece.
Laboratory of Marine Geology and Physical Oceanography, Department of Geology, University of Patras, 26504 Patras, Greece.
Mar Pollut Bull. 2021 Mar;164:111974. doi: 10.1016/j.marpolbul.2021.111974. Epub 2021 Jan 20.
Aerial and underwater imaging is being widely used for monitoring litter objects found at the sea surface, beaches and seafloor. However, litter monitoring requires a considerable amount of human effort, indicating the need for automatic and cost-effective approaches. Here we present an object detection approach that automatically detects seafloor marine litter in a real-world environment using a Region-based Convolution Neural Network. The neural network is trained on an imagery with 11 manually annotated litter categories and then evaluated on an independent part of the dataset, attaining a mean average precision score of 62%. The presence of other background features in the imagery (e.g., algae, seagrass, scattered boulders) resulted to higher number of predicted litter items compare to the observed ones. The results of the study are encouraging and suggest that deep learning has the potential to become a significant tool for automatically recognizing seafloor litter in surveys, accomplishing continuous and precise litter monitoring.
航空和水下成像技术正被广泛用于监测在海面、海滩和海底发现的垃圾物体。然而，垃圾监测需要大量人力，这表明需要自动且经济高效的方法。在此，我们提出一种目标检测方法，该方法使用基于区域的卷积神经网络在现实环境中自动检测海底海洋垃圾。该神经网络在包含11种人工标注垃圾类别的图像上进行训练，然后在数据集的独立部分上进行评估，平均精度得分达到62%。图像中其他背景特征（如藻类、海草、散落的巨石）的存在导致预测的垃圾物品数量比观察到的更多。该研究结果令人鼓舞，并表明深度学习有潜力成为调查中自动识别海底垃圾的重要工具，实现持续且精确的垃圾监测。
