Surface defect detection of aerospace sealing rings based on deep learning

Aiming at the problems of low aerospace seal ring surface defect detection efficiency of manual inspection and poor versatility of traditional image processing detection algorithms, two kinds of deep learning based surface defect detection algorithms for aerospace sealing rings are proposed. Firstly, aiming at the characteristic that most of the defects are small targets, the RetinaNet network that is more sensitive to small targets is selected as the basic architecture of the defect detection algorithm, and the MoGaA-RetinaNet algorithm is constructed by introducing the lightweight network MoGaA into the RetinaNet network. Secondly, in order to improve the detection accuracy, on the basis of MoGaA-RetinaNet, the newMoGaA backbone network is constructed using the decomposition convolution module to replace the depthwise convolution in the MoGaA backbone network, and the newMoGaA-RetinaNet algorithm is designed. Finally, the experiment results on the test set show that the MoGaA-RetinaNet algorithm has faster detection speed but slightly lower detection accuracy compared with the RetinaNet algorithm; the newMoGaA-RetinaNet algorithm achieves a good balance of detection accuracy and detection speed, Compared with those of RetinaNet algorithm, the detection accuracy rate increases by 4.5%, reaches to 92%; the detection speed increases by 55%, reaches to 31 frame/s; and the number of network parameters is reduced by 50%. The newly designed newMoGaA-RetinaNet algorithm can achieve fast and accurate detection of the seal ring surface defects. © 2021, Science Press. All right reserved.