Industrial CT is useful for defect detection, dimensional inspection and geometric analysis. While it does not meet the needs of industrial mass production, because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system, which is able to refresh the CT reconstructed slices to the detector frame frequency. This structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects’ translation, making it easier to be integrated into production line. All the detectors are arranged along the conveyor, and observe the objects in different angle of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time to adopt this source-detector layout strategy. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency.