用深度学习自动识别海底沉船，提升考古效率。
ShipwreckFinder: A QGIS Tool for Shipwreck Detection in Multibeam Sonar Data
- 基于深度学习模型，自动处理多波束声呐数据
- 在五大湖与爱尔兰海岸数据上训练，准确检测沉船
- 开源工具支持分割图与边界框输出，适合考古与海洋研究
本文介绍 ShipwreckFinder，一款用于从多波束声呐数据中检测沉船的开源 QGIS 插件。沉船是海洋历史的重要标志，传统上需人工检查地形数据，耗时且依赖专家。本工具可自动预处理水深数据，执行深度学习推理，通过阈值处理输出像素级分割掩码或预测边界框。其核心为在五大湖及爱尔兰沿海多种沉船数据上训练的深度学习模型，并采用合成数据生成扩充数据集多样性。实验表明，该工具在分割性能上优于基于深度学习的 ArcGIS 工具包和经典逆漏斗检测方法。插件开源地址：https://github.com/umfieldrobotics/ShipwreckFinderQGISPlugin。
原文摘要 · Abstract (English)
In this paper, we introduce ShipwreckFinder, an open-source QGIS plugin that detects shipwrecks from multibeam sonar data. Shipwrecks are an important historical marker of maritime history, and can be discovered through manual inspection of bathymetric data. However, this is a time-consuming process and often requires expert analysis. Our proposed tool allows users to automatically preprocess bathymetry data, perform deep learning inference, threshold model outputs, and produce either pixel-wise segmentation masks or bounding boxes of predicted shipwrecks. The backbone of this open-source tool is a deep learning model, which is trained on a variety of shipwreck data from the Great Lakes and the coasts of Ireland. Additionally, we employ synthetic data generation in order to increase the size and diversity of our dataset. We demonstrate superior segmentation performance with our open-source tool and training pipeline as compared to a deep learning-based ArcGIS toolkit and a more classical inverse sinkhole detection method. The open-source tool can be found at https://github.com/umfieldrobotics/ShipwreckFinderQGISPlugin.
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