论文标题
NDD20：一个大规模的少量海豚数据集，用于粗粒和细粒度分类
NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation
论文作者
论文摘要
我们介绍了诺森伯兰海豚数据集2020（NDD20），这是一个具有挑战性的图像数据集，用于针对粗糙和细粒实例细分和分类。该数据集是NDD的第一个版本，是为了响应计算机愿景的快速扩展到保护研究以及适合极端环境条件的现场剥夺系统的生产而创建的，该系统适合极端环境条件 - 这个区域很少。 NDD20包含大量的两种不同海豚物种的水图，用于传统的粗粒和细粒度分割。 NDD20中包含的所有数据均通过英国诺森伯兰海岸线附近的北海手动收集获得。我们使用使用NDD20训练并报告基线结果的标准深度学习网络体系结构进行了实验。
We introduce the Northumberland Dolphin Dataset 2020 (NDD20), a challenging image dataset annotated for both coarse and fine-grained instance segmentation and categorisation. This dataset, the first release of the NDD, was created in response to the rapid expansion of computer vision into conservation research and the production of field-deployable systems suited to extreme environmental conditions -- an area with few open source datasets. NDD20 contains a large collection of above and below water images of two different dolphin species for traditional coarse and fine-grained segmentation. All data contained in NDD20 was obtained via manual collection in the North Sea around the Northumberland coastline, UK. We present experimentation using standard deep learning network architecture trained using NDD20 and report baselines results.
