Abstract: Coated propellants (CPs) are extensively used in the dynamical systems of rockets and missiles. The appearance quality of the CPs has significant impact on the performance of the systems. To this end, a dynamic prior features-based deep learning framework for multidefect detection of CPs, such as shape, size, and surface defects, is put forth in this article: 1) An integrated deep model for deep classifier (DC)-based shape defect and deep segmentation network (DSN)-based size defect detection is introduced, which can remove redundant features among different tasks. Particularly, the features generated by the current iteration of the DSN, as dynamic prior features, act on the next iteration of the DC to accelerate the convergence rate, and 2) the dynamic features are also mapped to the convolutional autoencoder-based surface defect detection, which can guide the model to quickly focus on the CPs, while suppressing the repeated feature extraction of task-independent features. Experimental results on an image dataset from a real-world manufacturing line show that the proposed framework has the superiority in terms of the power consumption, detection efficiency, and detection accuracy.