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Instance Segmentation of Low-texture Industrial Parts Based on Deep Learning

机译：基于深度学习的低纹理工业零件的实例分割

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摘要

The instance segmentation of low-texture industrial parts is important for robot grasping operations in scattered environments. However, most of the current deep learning methods for instance segmentation rely heavily on the RGB information of the scene, which limits their application in low-texture scenes and some scenes where RGB information cannot be obtained; there are fewer point cloud datasets for industrial parts. The deep learning method based on point cloud is not ideal for the segmentation of scattered and stacked industrial parts with complex shapes. In this paper, a dataset for industrial parts is generated in a physical simulation environment, and a deep learning method for instance segmentation of low-texture industrial parts based on the point cloud is proposed. The simulation dataset experiment verifies that the method can achieve instance segmentation of low-texture industrial parts in scattered stacking scenes, and has strong robustness to point clouds with inconsistent densities and noise.
机译：低纹理工业部件的实例分割对于散射环境中的机器人掌握操作非常重要。然而，大多数当前的深度学习方法，例如分割的实例依赖于场景的RGB信息，这限制了它们在低纹理场景中的应用以及无法获得RGB信息的某些场景;工业部件的点云数据集减少。基于点云的深度学习方法并不理想的是具有复杂形状的分散和堆叠工业部件的分割。在本文中，提出了一种在物理仿真环境中产生工业部件的数据集，提出了一种基于点云的低纹理工业部件的实例分割的深度学习方法。模拟数据集实验验证该方法可以在分散的堆叠场景中实现低纹理工业部件的实例分割，并具有强大的鲁棒性，以指向不一致的密度和噪音。

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