Obstacle detection is a fundamental capability of an autonomous maritime
surface vessel (AMSV). State-of-the-art obstacle detection algorithms are based
on convolutional neural networks (CNNs). While CNNs provide higher detection
accuracy and fast detection speed, they require enormous amounts of data for
their training. In particular, the availability of domain-specific datasets is
a challenge for obstacle detection. The difficulty in conducting onsite
experiments limits the collection of maritime datasets. Owing to the logistic
cost of conducting on-site operations, simulation tools provide a safe and
cost-efficient alternative for data collection. In this work, we introduce
SimuShips, a publicly available simulation-based dataset for maritime
environments. Our dataset consists of 9471 high-resolution (1920x1080) images
which include a wide range of obstacle types, atmospheric and illumination
conditions along with occlusion, scale and visible proportion variations. We
provide annotations in the form of bounding boxes. In addition, we conduct
experiments with YOLOv5 to test the viability of simulation data. Our
experiments indicate that the combination of real and simulated images improves
the recall for all classes by 2.9%.

本文介绍了一种名为 SimuShips 的用于海上环境的基于模拟的数据集，其中包含各种类型的障碍物，经过了多种气象和照明条件的处理，并通过边界框的形式提供了注释。同时，我们使用 YOLOv5 进行实验，并发现将真实图像与模拟图像相结合可以提高所有类别的召回率。

SimuShips -- 一份高分辨率的模拟船只检测数据集，具备精确标注

SimuShips -- A High Resolution Simulation Dataset for Ship Detection with Precise Annotations

Physical intuition is pivotal for intelligent agents to perform complex
tasks. In this paper we investigate the passive acquisition of an intuitive
understanding of physical principles as well as the active utilisation of this
intuition in the context of generalised object stacking. To this end, we
provide: a simulation-based dataset featuring 20,000 stack configurations
composed of a variety of elementary geometric primitives richly annotated
regarding semantics and structural stability. We train visual classifiers for
binary stability prediction on the ShapeStacks data and scrutinise their
learned physical intuition. Due to the richness of the training data our
approach also generalises favourably to real-world scenarios achieving
state-of-the-art stability prediction on a publicly available benchmark of
block towers. We then leverage the physical intuition learned by our model to
actively construct stable stacks and observe the emergence of an intuitive
notion of stackability - an inherent object affordance - induced by the active
stacking task. Our approach performs well even in challenging conditions where
it considerably exceeds the stack height observed during training or in cases
where initially unstable structures must be stabilised via counterbalancing.

本文研究了智能 Agent 在执行复杂任务时所需的物理直觉，提供了一个基于模拟的数据集，利用视觉分类器进行物理稳定性预测，并通过利用被动 / 主动的物理直觉构造稳定的物体堆栈，并观察主动叠加任务引起的物体本质和对象自然属性 - 叠堆特征的引出。