# Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots

arXiv: https://arxiv.org/abs/2209.02379 · HTML: https://mlsanity.com/paper/2209.02379/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Deep Learning for Medical Anomaly Detection -- A Survey](https://mlsanity.com/paper/2012.02364/) (2020)
- [Pixel-wise Anomaly Detection in Complex Driving Scenes](https://mlsanity.com/paper/2103.05445/) (2021)
- [Learning Deep Features for One-Class Classification](https://mlsanity.com/paper/1801.05365/) (2018)
- [Weakly Supervised Silhouette-based Semantic Scene Change Detection](https://mlsanity.com/paper/1811.11985/) (2018)
- [ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments](https://mlsanity.com/paper/2103.05368/) (2021)
- [Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection](https://mlsanity.com/paper/1810.09111/) (2018)
- [Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization and Mapping Algorithms](https://mlsanity.com/paper/2203.03454/) (2022)
- [Multi Sensor Fusion for Navigation and Mapping in Autonomous Vehicles: Accurate Localization in Urban Environments](https://mlsanity.com/paper/2103.13719/) (2021)
- [Domain Invariant Siamese Attention Mask for Small Object Change Detection via Everyday Indoor Robot Navigation](https://mlsanity.com/paper/2203.15362/) (2022)
- [AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection](https://mlsanity.com/paper/2203.15195/) (2022)
- [Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection](https://mlsanity.com/paper/1602.07807/) (2016)
- [Analyzing General-Purpose Deep-Learning Detection and Segmentation Models with Images from a Lidar as a Camera Sensor](https://mlsanity.com/paper/2203.04064/) (2022)

## Built on by (most cited first, 3 of 3)

- [SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector](https://mlsanity.com/paper/2405.04788/) (2024)
- [Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients](https://mlsanity.com/paper/2412.07565/) (2024)
- [Offline Discovery of Interpretable Skills from Multi-Task Trajectories](https://mlsanity.com/paper/2602.01018/) (2026)

## Abstract (arXiv)

Automatic detection of visual anomalies and changes in the environment has been a topic of recurrent attention in the fields of machine learning and computer vision over the past decades. A visual anomaly or change detection algorithm identifies regions of an image that differ from a reference image or dataset. The majority of existing approaches focus on anomaly or fault detection in a specific class of images or environments, while general purpose visual anomaly detection algorithms are more scarce in the literature. In this paper, we propose a comprehensive deep learning framework for detecting anomalies and changes in a priori unknown environments after a reference dataset is gathered, and without need for retraining the model. We use the SuperPoint and SuperGlue feature extraction and matching methods to detect anomalies based on reference images taken from a similar location and with partial overlapping of the field of view. We also introduce a self-calibrating method for the proposed model in order to address the problem of sensitivity to feature matching thresholds and environmental conditions. To evaluate the proposed framework, we have used a ground robot system for the purpose of reference and query data collection. We show that high accuracy can be obtained using the proposed method. We also show that the calibration process enhances changes and foreign object detection performance