摘要
固体火箭发动机金属壳体的缺陷检测对于发动机状态的评估具有重要意义。文章提出一种基于机器视觉的检测方法,采用基于深度卷积生成对抗网络(DCGAN)的数据增强技术和基于卷积注意力模块(CBAM)的目标检测技术实现了发动机金属壳体表面缺陷的检测。首先基于已有的少量真实壳体缺陷数据,通过数据增强技术对数据集进行增强;再基于增强后的数据集,利用深度学习算法对缺陷目标进行识别和分类;最终实现对发动机壳体缺陷目标的检测。通过对多种壳体缺陷进行检测,验证了该方法的可行性,且该方法在不影响检测效率的前提下,识别准确率较传统检测方法提升5.7%,模型鲁棒性、泛化性较好,在实际工程中具有良好的应用前景。
The defect detection for the metal casing of solid rocket motor is essential in the assessment of motor conditions.A machine vision-based method for detecting surface defects on metal casings of solid rocket motors was proposed in this paper.The method employed data augmentation using deep convolutional generative adversarial networks(DCGAN),and target detection technique using a convolutional block attention module(CBAM).By utilizing limited real defect data and deep learning algorithms to augment the dataset,the method identified and classified defective targets.The method was tested against various types of casing defects,showing a 5.7%improvement in identification accuracy over conventional detection methods,while maintaining detection efficiency.The model also exhibits good robustness and generalization,indicating potential for practical engineering applications.
作者
孙旭阳
沈飞
姚怡倩
张潇誉
李晓豪
SUN Xuyang;SHEN Fei;YAO Yiqian;ZHANG Xiaoyu;LI Xiaohao(Xi’an Aerospace Propulsion Testing Technology Research Institute,Xi’an 710025,China)
出处
《航天器环境工程》
2025年第1期98-108,共11页
Spacecraft Environment Engineering
