Bucket fill factor is of paramount importance in measuring the productivity of construction vehicles, which is the percentage of materials loaded in the bucket within one scooping. Additionally, the locational information of the bucket is also indispensable for scooping trajectory planning. Some research has been conducted to measure it via state-of-the-art computer vision approaches, but their robustness against various environment conditions is not considered. The aim of this study is to fill this gap and six distinctive environment settings are included. Images captured by a stereo camera are used to generate point clouds before being structured into 3D maps. This novel preprocessing pipeline for deep learning is originally proposed and its feasibility has been validated through this study. Moreover, multitask learning is employed to exploit the positive relationship among two tasks: fill factor prediction and bucket detection. Therefore, after preprocessing, 3D maps are forwarded to a faster region with convolutional neural network incorporated with an improved residual neural network. The value of fill factor is acquired via a classification and probabilistic–based approach, which is novel, achieving an inspiring result (overall volume estimation accuracy: 95.23% and detection precision: 92.62%) at the same time.

中文翻译：

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基于神经网络的工程车辆填充因子估计和铲斗检测方法

铲斗填充系数对于衡量工程车辆的生产率至关重要，它是一勺内装载在铲斗中的材料百分比。此外，铲斗的位置信息对于铲斗轨迹规划也是必不可少的。已经进行了一些研究以通过最先进的计算机视觉方法对其进行测量，但并未考虑它们对各种环境条件的鲁棒性。本研究的目的是填补这一空白，包括六种独特的环境设置。立体相机捕获的图像用于在被构建为 3D 地图之前生成点云。这种用于深度学习的新型预处理管道最初是提出的，其可行性已通过本研究得到验证。而且，多任务学习用于利用两个任务之间的正相关关系：填充因子预测和桶检测。因此，在预处理之后，3D 地图被转发到一个更快的区域，卷积神经网络结合了改进的残差神经网络。填充因子的值是通过基于分类和概率的方法获得的，这是一种新颖的方法，同时取得了令人鼓舞的结果（整体体积估计精度：95.23% 和检测精度：92.62%）。