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In this work, we introduce a method and present an improved neural work toperform product re-identification, which is an essential core function of afully automated product defect detection system. Our method is based on featuredistance. It is the combination of feature extraction neural networks, such asVGG16, AlexNet, with an image search engine - Vearch. The dataset that we usedto develop product re-identification systems is a water-bottle dataset thatconsists of 400 images of 18 types of water bottles. This is a small dataset,which was the biggest challenge of our work. However, the combination of neuralnetworks with Vearch shows potential to tackle the product re-identificationproblems. Especially, our new neural network - AlphaAlexNet that a neuralnetwork was improved based on AlexNet could improve the productionidentification accuracy by four percent. This indicates that an idealproduction identification accuracy could be achieved when efficient featureextraction methods could be introduced and redesigned for image featureextractions of nearly identical products. In order to solve the biggestchallenges caused by the small size of the dataset and the difficult nature ofidentifying productions that have little differences from each other. In ourfuture work, we propose a new roadmap to tackle nearly-identical productionidentifications: to introduce or develop new algorithms that need very fewimages to train themselves.

Product Re-identification System in Fully Automated Defect Detection