# Find the Assembly Mistakes: Error Segmentation for Industrial Applications

arXiv: https://arxiv.org/abs/2408.12945 · HTML: https://mlsanity.com/paper/2408.12945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [Learning to Describe Differences Between Pairs of Similar Images](https://mlsanity.com/paper/1808.10584/) (2018)
- [IndustReal: A Dataset for Procedure Step Recognition Handling Execution Errors in Egocentric Videos in an Industrial-Like Setting](https://mlsanity.com/paper/2310.17323/) (2023)
- [PREGO: online mistake detection in PRocedural EGOcentric videos](https://mlsanity.com/paper/2404.01933/) (2024)
- [TINYCD: A (Not So) Deep Learning Model For Change Detection](https://mlsanity.com/paper/2207.13159/) (2022)
- [ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection](https://mlsanity.com/paper/2305.16713/) (2023)
- [VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON](https://mlsanity.com/paper/2306.07890/) (2023)
- [The Change You Want to See (Now in 3D)](https://mlsanity.com/paper/2308.10417/) (2023)
- [ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation](https://mlsanity.com/paper/2403.16400/) (2024)
- [Supervised Representation Learning towards Generalizable Assembly State Recognition](https://mlsanity.com/paper/2408.11700/) (2024)

## Abstract (arXiv)

Recognizing errors in assembly and maintenance procedures is valuable for industrial applications, since it can increase worker efficiency and prevent unplanned down-time. Although assembly state recognition is gaining attention, none of the current works investigate assembly error localization. Therefore, we propose StateDiffNet, which localizes assembly errors based on detecting the differences between a (correct) intended assembly state and a test image from a similar viewpoint. StateDiffNet is trained on synthetically generated image pairs, providing full control over the type of meaningful change that should be detected. The proposed approach is the first to correctly localize assembly errors taken from real ego-centric video data for both states and error types that are never presented during training. Furthermore, the deployment of change detection to this industrial application provides valuable insights and considerations into the mechanisms of state-of-the-art change detection algorithms. The code and data generation pipeline are publicly available at: https://timschoonbeek.github.io/error_seg.