ABSTRACT

In this work, realistically drawn objects are identified on digital maps by convolutional neural networks. For the first two experiments, 6200 images were retrieved from Pinterest. While alternating image input options, two binary classifiers based on Xception and InceptionResNetV2 were trained to separate maps and pictorial maps. Results showed that the accuracy is 95–97% to distinguish maps from other images, whereas maps with pictorial objects are correctly classified at rates of 87–92%. For a third experiment, bounding boxes of 3200 sailing ships were annotated in historic maps from different digital libraries. Faster R-CNN and RetinaNet were compared to determine the box coordinates, while adjusting anchor scales and examining configurations for small objects. A resulting average precision of 32% was obtained for Faster R-CNN and of 36% for RetinaNet. Research outcomes are relevant for trawling map images on the Internet and for enhancing the advanced search of digital map catalogues.

摘要

在这项工作中，通过卷积神经网络在数字地图上识别真实绘制的对象。对于前两个实验，从 Pinterest 检索了 6200 张图像。在交替图像输入选项的同时，训练了两个基于 Xception 和 InceptionResNetV2 的二元分类器来分离地图和图片地图。结果表明，区分地图与其他图像的准确率为 95-97%，而带有图形对象的地图的正确分类率为 87-92%。在第三个实验中，3200 艘帆船的边界框在来自不同数字图书馆的历史地图中进行了注释。比较 Faster R-CNN 和 RetinaNet 以确定框坐标，同时调整锚点比例并检查小物体的配置。Faster R-CNN 的平均精度为 32%，RetinaNet 的平均精度为 36%。研究成果与在 Internet 上拖网地图图像和增强数字地图目录的高级搜索相关。