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Industrial CT is useful for defect detection, dimensional inspection andgeometric analysis. While it does not meet the needs of industrial massproduction, because of its time-consuming imaging procedure. This articleproposes a novel stationary real-time CT system with multiple X-ray sources anddetectors, which is able to refresh the CT reconstructed slices to the detectorframe frequency. This kind of structure avoids the movement of the X-raysources and detectors. Projections from different angles can be acquired withthe objects' translation, which makes it easier to be integrated into pipeline.All the detectors are arranged along the conveyor, and observe the objects indifferent angle of view. With the translation of objects, their X-rayprojections are obtained for CT reconstruction. To decrease the mechanical sizeand reduce the number of X-ray sources and detectors, the FBP reconstructionalgorithm was combined with deep-learning image enhancement. Medical CT imageswere applied to train the deep-learning network for its quantity advantage incomparison with industrial ones. It is the first time to adopt thissource-detector layout strategy. Data augmentation and regularization were usedto elevate the generalization of the network. Time consumption of the CTimaging process was also calculated to prove its high efficiency. It is aninnovative design for the 4th industrial revolution, providing an intelligentquality inspection solution for digital production.

A novel design of industrial real-time CT system based on sparse-view reconstruction and deep-learning image enhancement