Đuraš Antun, Wolf Ben J, Ilioudi Athina, Palunko Ivana, De Schutter Bart
Authors are with the Laboratory for Intelligent Autonomous Systems (LARIAT), Department of Electrical Engineering and Computing, University of Dubrovnik, Dubrovnik, Croatia.
Author is with the Bernoulli Institute, Faculty of Science and Engineering, University of Groningen, Groningen, Netherlands.
Sci Data. 2024 Aug 24;11(1):921. doi: 10.1038/s41597-024-03759-2.
Robust object detection is crucial for automating underwater marine debris collection. While supervised deep learning achieves state-of-the-art performance in discriminative tasks, replicating this success on underwater data is challenging. The generalization of these methods suffers due to a lack of available annotated data considering different sources of variation in the unstructured underwater environment and imaging conditions. In this paper, we present the Seaclear Marine Debris Dataset, the first publicly available shallow-water marine debris dataset annotated for instance segmentation/object detection. The dataset contains 8610 images collected using ROVs at multiple locations and with different cameras, annotated for 40 object categories, encompassing not only litter but also observed animals, plants, and robot parts. As part of the technical validation, we provide baseline results for object detection using Faster RCNN and YOLOv6 models. Furthermore, we demonstrate the non-triviality of generalizing the trained model performance to unseen sites and cameras due to domain shift. This underscores the value of the presented dataset in further developing robust models for underwater debris detection.
强大的目标检测对于水下海洋垃圾收集自动化至关重要。虽然监督式深度学习在判别任务中取得了领先的性能，但要在水下数据上复制这一成功却具有挑战性。由于缺乏考虑非结构化水下环境和成像条件中不同变化来源的可用标注数据，这些方法的泛化能力受到影响。在本文中，我们展示了Seaclear海洋垃圾数据集，这是首个公开可用的针对实例分割/目标检测进行标注的浅水海洋垃圾数据集。该数据集包含使用遥控潜水器（ROV）在多个地点并使用不同相机收集的8610张图像，针对40个目标类别进行了标注，不仅包括垃圾，还包括观察到的动物、植物和机器人部件。作为技术验证的一部分，我们提供了使用Faster RCNN和YOLOv6模型进行目标检测的基线结果。此外，我们证明了由于域转移，将训练模型的性能推广到未见地点和相机并非易事。这凸显了所展示的数据集在进一步开发强大的水下垃圾检测模型方面的价值。
