Kilian Wilhelm Johannes, Prinsloo Jaco, Vosloo Jan, Taljaard Stéphan
Faculty of Engineering, North-West University, Potchefstroom, South Africa.
Front Artif Intell. 2024 Dec 13;7:1453931. doi: 10.3389/frai.2024.1453931. eCollection 2024.
Many industries utilize deep learning methods to increase efficiency and reduce costs. One of these methods, image segmentation, is used for object detection and recognition in localization and mapping. Segmentation models are trained using labeled datasets; however, manually creating datasets for every application, including deep-level mining, is time-consuming and typically expensive. Recently, many papers have shown that using synthetic datasets (digital recreations of real-world scenes) for training produces highly-accurate segmentation models. This paper proposes a synthetic segmentation dataset generator using a 3D modeling framework and raycaster. The generator was applied to a deep-level mining case study and produced a dataset containing labeled images of scenes typically found in this environment, therefore removing the requirement to create the dataset manually. Validation showed high accuracy segmentation after model training using the generated dataset (compared to other applications that use real-world datasets). Furthermore, the generator can be customized to produce datasets for many other applications.
许多行业都在利用深度学习方法来提高效率和降低成本。其中一种方法，即图像分割，用于定位和映射中的目标检测与识别。分割模型使用标记数据集进行训练；然而，为每个应用程序（包括深层挖掘）手动创建数据集既耗时又通常成本高昂。最近，许多论文表明，使用合成数据集（真实世界场景的数字再现）进行训练可以产生高精度的分割模型。本文提出了一种使用3D建模框架和光线投射器的合成分割数据集生成器。该生成器被应用于一个深层挖掘案例研究，并生成了一个包含该环境中常见场景的标记图像的数据集，从而消除了手动创建数据集的需求。验证表明，使用生成的数据集进行模型训练后，分割精度很高（与使用真实世界数据集的其他应用程序相比）。此外，该生成器可以定制，以生成适用于许多其他应用程序的数据集。
