论文标题
垃圾桶：朝着视觉检测海洋碎片的语义细分数据集
TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris
论文作者
论文摘要
本文介绍了垃圾桶，这是一个大型数据集，由从各种来源收集的水下垃圾图像组成，使用边界盒和分割标签进行了注释，以开发海洋碎片的强大探测器。该数据集有两个版本，即垃圾桶 - 材料和垃圾桶，对应于不同的对象类配置。最终的目标是开发适用于机器机器人部署的高效且准确的垃圾检测方法。除了有关垃圾桶数据集的构建和采购的信息，我们还提供了从蒙版R-CNN分割的初始结果，并从更快的R-CNN中介绍了对象检测。这些并不代表最佳检测结果，但为实例分割和垃圾桶数据集中的对象检测提供了初始基线。
This paper presents TrashCan, a large dataset comprised of images of underwater trash collected from a variety of sources, annotated both using bounding boxes and segmentation labels, for development of robust detectors of marine debris. The dataset has two versions, TrashCan-Material and TrashCan-Instance, corresponding to different object class configurations. The eventual goal is to develop efficient and accurate trash detection methods suitable for onboard robot deployment. Along with information about the construction and sourcing of the TrashCan dataset, we present initial results of instance segmentation from Mask R-CNN and object detection from Faster R-CNN. These do not represent the best possible detection results but provides an initial baseline for future work in instance segmentation and object detection on the TrashCan dataset.
