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.

空中和水下成像被广泛用于监视在海面，海滩和海底发现的垃圾对象。但是，垃圾监测需要大量的人力，这表明需要自动且具有成本效益的方法。在这里，我们提出了一种对象检测方法，该方法使用基于区域的卷积神经网络在实际环境中自动检测海底海洋垃圾。该神经网络在具有11个手动注释的垃圾分类的图像上进行训练，然后在数据集的独立部分进行评估，平均平均精度得分为62％。图像中其他背景特征（例如藻类，海草，散落的巨石）的存在导致与观察到的相比，预测的垫料数量更高。