The development of autonomous detection technology is imperative in the field of construction. The bucket fill factor is one of the main indicators for evaluating the productivity of construction vehicles. Bucket detection is a prerequisite for bucket trajectory planning. However, previous studies have been conducted under ideal environments, a specific single environment, and several normal environments without considering the actual harsh environments at construction sites. Therefore, seven extreme environments are set in this paper to fill this gap, and an effective method is proposed. First, a novel framework for image restoration under extreme environments is proposed. It applies to all tasks conducted by vision on construction sites. Second, a combination of segmentation and classification networks is used for the first time in this area. Multitask learning is used to discover a positive correlation between fill factor estimation and bucket detection. Furthermore, probabilistic methods and transfer learning were introduced, and excellent results were achieved (97.40% accuracy in fill factor estimation and 99.76% accuracy in bucket detection for seven extreme environments).

中文翻译：

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极端环境下工程车辆填充因子估计和铲斗检测的深度学习方法

建筑领域自主检测技术的发展势在必行。铲斗填充系数是评价工程车辆生产率的主要指标之一。铲斗检测是铲斗轨迹规划的前提。然而，以往的研究都是在理想环境、特定单一环境和几种正常环境下进行的，没有考虑施工现场实际的恶劣环境。因此，本文设置了七种极端环境来填补这一空白，并提出了一种有效的方法。首先，提出了一种极端环境下图像恢复的新颖框架。它适用于建筑工地上通过视觉执行的所有任务。其次，该领域首次使用了分割和分类网络的组合。多任务学习用于发现填充因子估计和桶检测之间的正相关性。此外，还引入了概率方法和迁移学习，取得了优异的结果（填充因子估计准确率为97.40％，七种极端环境下的桶检测准确率为99.76％）。