As industrial computerized tomography (ICT) is widely used in the non-destructive testing of a solid rocket motor (SRM), the problem of how to automatically discriminate defect types and measure defect sizes with high accuracy in ICT images of SRM grains needs to be urgently solved. To address the problems of low manual recognition efficiency and data utilization in the ICT image analysis of SRM grains, we proposed an automated defect analysis (ADA) system for ICT images of SRM grains based on the YOLO-V4 model. Using the region proposal of the YOLO-V4 model, a region growing algorithm with automatic selection of seed points was proposed to segment the defect areas of the ICT images of grains. Defect sizes were automatically measured based on the automatic determination of defect types by the YOLO-V4 model. In this paper, the image recognition performance of YOLO-V4, YOLO-V3, and Faster R-CNN models were compared. The results show that the average accuracy (mAP) of the YOLO-V4 model is more than 15% higher than that of the YOLO-V3 and Faster R-CNN models, the F1-score is 0.970, and the detection time per image is 0.152 s. The ADA system can measure defect sizes with an error of less than 10%. Tests show that the system proposed in this paper can automatically analyze the defects in ICT images of SRM grains and has certain application value.

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As the nondestructive testing method based on industrial computerized tomography (ICT) is widely used in solid rocket motor (SRM) propellant defect detection, the demand for a corresponding image processing algorithm is increasing. In order to extract better defect information on SRM propellants, we studied the edge detection algorithm for their ICT images. This paper proposes a guided filter-based edge detection algorithm for ICT images of SRM propellants with much noise. The algorithm innovatively uses guided filters to converge the detection results of type I edges with good edge continuity to type II edges with clear positioning. The obtained type III edges have good edge continuity and clear positioning. The experimental results show that the proposed algorithm can achieve edge detection effectively.