A Deep Detection Model based on Multi-task Learning for Appearance Defect of Solid Propellants

Abstract

Solid propellants (SPs), as a high-energy material, are commonly used in military and industrial power systems, such as solid rocket and missiles. The SPs, however, confronts severe difficulties of inevitable defects while being made, thus bringing about the significance of inspection. However, the literatures before typically tackled this problem separately, which subsequently combines different models for the variety of defect patterns. Despite of the effectiveness, this act of matters usually brings excessive complexity and additionally computational burden. In this article, we managed to solve this problem in an integrated framework, which unite both the size detection task and shape detection task at the same time, but with different training strategies. To be specific, our framework is mostly consisted of two stage. Firstly, the SPs region is output using a semantic segmentation network, and size measurements are completed with traditional image processing to determine the size defects of the SPs. Then, the depth features of the segmentation network are combined with the semantic segmentation map to make a spatial attention mechanism, which is input to the deep classifier to complete the shape defect detection. The focus of model is gradually shifted from the segmentation task to the classification task as the number of training sessions increases by introducing dynamic balancing factors. The experimental results show that the multi-task learning approach can greatly improve the generalization and robustness of the model, and the accuracy and speed are improved for appearance defect detection of SPs.