Due to complicated backgrounds and unclear target orientation, automated object detection is difficult in the field of archaeology. Most of the current convolutional neural network (CNN) object-oriented detection techniques are based on a faster region-based CNN (R-CNN) and other one-stage detectors that often lack adequate processing speeds and detection accuracies. Recently, the two-stage detector Mask R-CNN technique achieved impressive results in object detection and instance segmentation problems and was successfully applied in the analysis of archaeological airborne laser scanning (ALS) data. In this study, we outline a modified Mask R-CNN technique that reliably and efficiently detects relict charcoal hearth (RCH) sites on light detection and ranging (LiDAR) data-based digital elevation models (DEMs). Using image augmentation and image preprocessing steps combined with the deep learning-based adaptive gradient method with a dynamic bound on the learning rate (AdaBound) optimization technique, we could improve the model's accuracy and significantly reduce its training time. We use DEMs based on high-resolution LiDAR data and the visualization for archaeological topography (VAT) technique that give images with a very strong contrast of the terrain and the outline of the sites of interest in the North German Lowland. Therefore, the model can identify RCH sites with an average recall of 83% and an average precision of 87%. Techniques such as the modified Mask R-CNN method outlined here will help to greatly improve our knowledge about archaeological site densities in the realm of historical charcoal production and past human-landscape interactions. This method provides an accurate, time-efficient and bias-free large-scale site mapping option not only for the North German Lowland but potentially for other landscapes as well.

中文翻译：

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一种改进的基于掩模区域的卷积神经网络方法，用于在北德低地的高分辨率光检测和测距衍生数字高程模型上自动检测考古遗址

由于背景复杂，目标方向不明确，在考古领域很难实现自动物体检测。当前的大多数卷积神经网络 (CNN) 面向对象检测技术都基于更快的基于区域的 CNN (R-CNN) 和其他通常缺乏足够处理速度和检测精度的单级检测器。最近，两阶段检测器 Mask R-CNN 技术在对象检测和实例分割问题上取得了令人瞩目的成果，并成功应用于考古机载激光扫描 (ALS) 数据的分析。在这项研究中，我们概述了一种改进的 Mask R-CNN 技术，该技术可以在基于光检测和测距 (LiDAR) 数据的数字高程模型 (DEM) 上可靠有效地检测残留木炭炉 (RCH) 站点。使用图像增强和图像预处理步骤，结合基于深度学习的自适应梯度方法和学习率动态边界 (AdaBound) 优化技术，我们可以提高模型的准确性并显着减少其训练时间。我们使用基于高分辨率 LiDAR 数据的 DEM 和考古地形 (VAT) 技术的可视化，这些技术提供的图像具有非常强烈的地形对比度和北德低地感兴趣地点的轮廓。因此，该模型可以识别出 RCH 站点，平均召回率为 83%，平均精度为 87%。此处概述的改进的 Mask R-CNN 方法等技术将有助于大大提高我们对历史木炭生产和过去人与景观相互作用领域中考古遗址密度的了解。这种方法不仅为北德低地提供了一种准确、省时且无偏差的大规模站点制图选项，还可能用于其他景观。