The internal assembly correctness of industrial products directly affects their performance and service life. Industrial products are usually protected by opaque housing, so most internal detection methods are based on X-rays. Since the dense structural features of industrial products, it is challenging to detect the occluded parts only from projections. Limited by the data acquisition and reconstruction speeds, CT-based detection methods do not achieve real-time detection. To solve the above problems, we design an end-to-end single-projection 3D segmentation network. For a specific product, the network adopts a single projection as input to segment product components and output 3D segmentation results. In this study, the feasibility of the network was verified against data containing several typical assembly errors. The qualitative and quantitative results reveal that the segmentation results can meet industrial assembly real-time detection requirements and exhibit high robustness to noise and component occlusion.

中文翻译：

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3D M-Net：基于单投影的特定对象3D分割网络

工业产品内部装配的正确性直接影响其性能和使用寿命。工业产品通常受到不透明外壳的保护，因此大多数内部检测方法都基于 X 射线。由于工业产品的密集结构特征，仅从投影中检测被遮挡的部分具有挑战性。受数据采集和重建速度的限制，基于 CT 的检测方法无法实现实时检测。为了解决上述问题，我们设计了一个端到端的单投影 3D 分割网络。对于特定的产品，网络采用单个投影作为输入来分割产品组件并输出 3D 分割结果。在这项研究中，网络的可行性根据包含几个典型装配错误的数据进行了验证。