Applying Deep Learning for automated visual verification of manual bracket installations

Abstract In this work, we explore a deep learning based automated visual inspection and verification algorithm, based on the Siamese Neural Network architecture. This is explored alongside the typical Convolutional Neural Network. Consideration is also given to how the input pairs of images can affect the performance of the Siamese Neural Network, dependent on the nature of the dataset used. A case study is provided from the aircraft manufacturing industry focusing on the visual inspection of wing bracket installations. A novel voting scheme specific to the Siamese Neural Network which sees a single model vote on multiple reference images is validated in this work. The results obtained show great potential for the use of the Siamese Neural Network for automated visual inspection and verification tasks in aircraft manufacturing and similar industries where there is often a scarcity of training data available.