用深度学习自动检查飞机支架安装，解决数据少时的验证难题。
Applying Deep Neural Networks to automate visual verification of manual bracket installations in aerospace
- 采用孪生网络+多参考图像投票新策略，提升判断准确性。
- 在数据稀缺条件下仍实现高精度，验证效果优于传统方法。
- 适合航空航天领域低样本场景下的自动化质检应用。
本文提出一种基于孪生神经网络的自动化视觉检测与验证算法，用于航空航天领域支架安装的手动校验。研究了输入图像对对模型性能的影响，并对比了卷积神经网络与孪生网络的表现。通过迁移学习和集成方法进一步提升模型性能，提出一种针对孪生网络的新型投票机制：单个模型对多个参考图像进行判断，不同于传统多模型对同一样本投票的方式。实验在公开的 Omniglot 数据集上验证了该方法的有效性。结果表明，该方法在训练数据有限的情况下仍具有显著潜力，所提相似性投票策略显著提升了模型表现。据我们所知，这是首次在航空航天支架自动验证任务中系统性地应用深度神经网络进行研究。
原文摘要 · Abstract (English)
In this work, we explore a deep learning based automated visual inspection and verification algorithm, based on the Siamese Neural Network architecture. Consideration is also given to how the input pairs of images can affect the performance of the Siamese Neural Network. The Siamese Neural Network was explored alongside Convolutional Neural Networks. In addition to investigating these model architectures, additional methods are explored including transfer learning and ensemble methods, with the aim of improving model performance. We develop a novel voting scheme specific to the Siamese Neural Network which sees a single model vote on multiple reference images. This differs from the typical ensemble approach of multiple models voting on the same data sample. The results obtained show great potential for the use of the Siamese Neural Network for automated visual inspection and verification tasks when there is a scarcity of training data available. The additional methods applied, including the novel similarity voting, are also seen to significantly improve the performance of the model. We apply the publicly available omniglot dataset to validate our approach. According to our knowledge, this is the first time a detailed study of this sort has been carried out in the automatic verification of installed brackets in the aerospace sector via Deep Neural Networks.
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