Rock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

岩石艺术雕刻，最好被形容为岩画，是通过去除岩石表面的一部分以产生负浮雕来制作的。在北欧斯堪的纳维亚半岛的北欧青铜时代（公元前1700年至550年）期间，这一传统尤为强大，有超过20,000艘船和数千人，动物，货车等。这种生动而引人入胜的资料提供了定量的数据，具有理解青铜时代的社会结构和意识形态的巨大潜力。提供技术上可能最好的文档以及自动对图像进行识别和分类的能力将有助于充分利用斯堪的纳维亚半岛南部和其他地方的岩画的研究潜力。因此，我们尝试基于一种新方法生成的数据，训练一种使用更快的基于区域的卷积神经网络（Faster-RCNN）来定位和分类图像对象的模型，以改善3D文档内容的可视化。与旧方法相比，新创建的3D岩石艺术文档层可提供当前可用的最佳数据，并减少了内切偏差。基于输入图像的几种模型进行了训练，输入图像带有使用不同参数生成的边界框来标注，以找到最佳解决方案。该数据包括408个岩画遗址扫描中的4305个单独图像。为了增强模型并丰富训练数据，我们使用了数据扩充和转移学习。成功的模型在船只和圈子以及人物和车轮上的表现都异常出色。这项工作是一项跨学科的工作，引起了有关考古学，数字人文科学和人工智能的重要思考。训练有素的模型所代表的思考和成功为岩石艺术的未来研究开辟了新的途径。为了增强模型并丰富训练数据，我们使用了数据扩充和转移学习。成功的模型在船只和圈子以及人物和车轮上的表现都异常出色。这项工作是一项跨学科的工作，引起了有关考古学，数字人文科学和人工智能的重要思考。训练有素的模型所代表的思考和成功为岩石艺术的未来研究开辟了新的途径。为了增强模型并丰富训练数据，我们使用了数据扩充和转移学习。成功的模型在船只和圈子以及人物和车轮上的表现都异常出色。这项工作是一项跨学科的工作，引起了有关考古学，数字人文科学和人工智能的重要思考。训练有素的模型所代表的思考和成功为岩石艺术的未来研究开辟了新的途径。