标题： 基于单张图像的散装货物堆几何形状建模和体积估算 显示英文标题

标题： Geometric Shape Modelling and Volume Estimation of Dry Bulk Cargo Piles using a Single Image

摘要： 岸上货物堆体积的估算对于航运和矿业公司以及公共机构在实时物流规划、商业智能、陆海运输服务及政府监管方面具有经济重要性。 在遥感文献中，通过依赖物体的照明特性从单个图像构建几何形状来估算堆体积，或者采用立体成像技术从图像对构建数字高程模型。 我们提出了一种新颖的方法，从单个光学图像中估算体积，在这项工作中，我们利用材料属性，该属性通过休止角将堆的底面尺寸与其高度相关联。 在材料文献中，这通常针对固定底面及其不同材料的\textit{原位}体积估算进行了广泛研究。 然而，在这项工作中，我们通过固定高度模型数学建模了堆的几何形状。 这是合适的，因为形成堆的卸料起重机臂只能上升到一定高度，并且在卸料过程中通常在水平面上移动。 在对卸料设备作直线运动时，对固定高度下的规则堆的几何形状进行数学建模后，我们提供了用于估算体积的闭式公式。 除了奠定数学基础外，我们还在石英砂露天散货存储设施的真实光学遥感数据上测试了这种方法，并展示了结果。 我们获得的估计存储设施总散货存储体积的准确率为$95\%$。 这是一个初步的研究，未来将与应用机器学习方法或当前最先进的方法集成，以处理更复杂的干散货堆体积估算场景。 显示英文摘要

摘要： Volume estimation of onshore cargo piles is of economic importance for shipping and mining companies as well as public authorities for real-time planning of logistics, business intelligence, transport services by land or sea and governmental oversight. In remote sensing literature, the volume of pile is estimated by relying on the illumination property of object to construct the geometric shape from a single image, alternatively, stereographic imaging for construction of a digital elevation model from pairs of images. In a fresh perspective, we propose a novel approach for estimating volume from a single optical image in this work where we use the material property, which relates the base dimensions of the pile to its height through the critical angle of repose. In materials literature, often this is well-studied for fixed base and their \textit{in situ} volume estimation for different materials. In this work, however, we mathematically model the geometric shape of the pile through a fixed height model. This is appropriate because the unloading crane arm that forms the pile can rise only up to a certain height and generally moved in the horizontal plane during unloading of the material. After mathematically modelling the geometric shape of regular piles for fixed heights under rectilinear motion of unloader, we provide closed form formula to estimate their volume. Apart from laying the mathematical foundations, we also test it on real optical remote sensing data of an open bulk cargo storage facility for silica sand and present the results. We obtain an accuracy of $95\%$ in estimating the total bulk storage volume of the storage facility. This is a first demonstration study and will be integrated with applied machine learning approaches or current state-of-art approaches in the future for more complex scenarios for estimating dry bulk cargo pile volume.