# Product Re-identification System in Fully Automated Defect Detection

arXiv: https://arxiv.org/abs/2112.10324 · HTML: https://mlsanity.com/paper/2112.10324/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Dual Path Networks](https://mlsanity.com/paper/1707.01629/) (2017)
- [On the Unreasonable Effectiveness of Centroids in Image Retrieval](https://mlsanity.com/paper/2104.13643/) (2021)
- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
- [Fashion Landmark Detection in the Wild](https://mlsanity.com/paper/1608.03049/) (2016)
- [Facial Emotion Recognition: State of the Art Performance on FER2013](https://mlsanity.com/paper/2105.03588/) (2021)
- [Person Re-Identification with a Locally Aware Transformer](https://mlsanity.com/paper/2106.03720/) (2021)
- [The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform](https://mlsanity.com/paper/1908.07389/) (2019)
- [Link and code: Fast indexing with graphs and compact regression codes](https://mlsanity.com/paper/1804.09996/) (2018)
- [Segmentation of Arterial Walls in Intravascular Ultrasound Cross-Sectional Images Using Extremal Region Selection](https://mlsanity.com/paper/1806.03695/) (2018)
- [Evaluation of Distance Measures for Feature based Image Registration using AlexNet](https://mlsanity.com/paper/1907.12921/) (2019)
- [Potential of deep features for opinion-unaware, distortion-unaware, no-reference image quality assessment](https://mlsanity.com/paper/1911.11903/) (2019)
- [Neural Networks for Fashion Image Classification and Visual Search](https://mlsanity.com/paper/2005.08170/) (2020)

## Built on by (most cited first, 2 of 2)

- [Application Of ADNN For Background Subtraction In Smart Surveillance System](https://mlsanity.com/paper/2301.00264/) (2022)
- [Two-stage Synthetic Supervising and Multi-view Consistency Self-supervising based Animal 3D Reconstruction by Single Image](https://mlsanity.com/paper/2311.13199/) (2023)

## Abstract (arXiv)

In this work, we introduce a method and present an improved neural work to perform product re-identification, which is an essential core function of a fully automated product defect detection system. Our method is based on feature distance. It is the combination of feature extraction neural networks, such as VGG16, AlexNet, with an image search engine - Vearch. The dataset that we used to develop product re-identification systems is a water-bottle dataset that consists 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 neural networks with Vearch shows potential to tackle the product re-identification problems. Especially, our new neural network - AlphaAlexNet that a neural network was improved based on AlexNet could improve the production identification accuracy by four percent. This indicates that an ideal production identification accuracy could be achieved when efficient feature extraction methods could be introduced and redesigned for image feature extractions of nearly identical products. In order to solve the biggest challenges caused by the small size of the dataset and the difficult nature of identifying productions that have little differences from each other. In our future work, we propose a new roadmap to tackle nearly-identical production identifications: to introduce or develop new algorithms that need very few images to train themselves.