Surface defects detection algorithm for a small sample of sealing rings for aerospace based on deep learning

Abstract

When using visual image technology to detect surface defects of sealing rings for aerospace, traditional image processing algorithms are constrained by surface defects types, scale differences, illumination variations, and non-uniformities of surface image. As a result, the application of each algorithm is relatively limited. To solve the problems, a general deep learning algorithm for surface defects detection of sealing rings is proposed. Texture features of defects, gradient features of edges and contour features are selected as main features of surface defects of sealing rings differentiated from the background, and a new cascade modular backbone network is design for extraction of these features. The average aspect ratio of label box for defects among dataset of defect images of sealing rings is calculated and aspect ratio distribution is counted to obtain the proportional parameter for generating prior box. Based on the calculated proportional parameters of prior box and scale features of defects, multi-scale defects detection network is designed. Meanwhile, defects classification and position regression compound loss function are defined. In the end, weighting parameters of the network are updated on the basis of end-to-end training. Experimental result shows that the accuracy of proposed algorithm for defects detection reach 94.03% under the condition that the original defects sample images is 107 and confidence is 0.6. Compared with RefineDet network, the depth of backbone network of feature extraction increases by 78% while the scale of parameters reduces by 84.82% and the accuracy of defects detection reduces by 3.01%.