Optimization Method for Classifier Output Repeatability Based on Siamese Networks

In industrial surface Quality Control (QC) scenarios, deep classification neural networks are widely used to classify product images for qualified judgment or quality grading. However, surface QC equipment equipped with deep classification neural networks must meet Attribute Reproducibility and Repeatability (AR&R) assessment requirements. Perturbations in product images, caused by assembly tolerance, equipment vibrations, and other factors, lead to variations in position, angle, brightness, and blurring. These perturbations result in inconsistent classification outputs, causing the surface QC equipment to fail the AR&R assessment, a problem referred to as the network output reproducibility issue. To address this issue, this study proposes a training method for classification neural networks based on Siamese networks. The Siamese primary network is trained using original samples for supervised learning to learn correct classification categories. The Siamese secondary network copies the weights of the primary network via exponential smoothing and generates feature embeddings of perturbed samples corresponding to the original ones. These embeddings are used for comparative learning training of the primary network, enabling it to output consistent classification probabilities for both original and perturbed sample inputs. During inference, only the primary network is retained for product defect classification. The results show that the classification accuracy reaches 99.346 2%, with a classification probability variance of 0.001 016. The described method effectively improves the output reproducibility of deep classification neural networks for industrial product image classification by reducing classification probability variance and enhancing accuracy.