Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots

TL;DR

This paper presents a deep learning framework for autonomous visual anomaly and change detection in unknown environments, featuring self-calibration to improve robustness without retraining, demonstrated on a ground robot system.

Contribution

The paper introduces a novel self-calibrating method integrated with a deep learning framework for environment-agnostic anomaly detection without retraining.

Findings

High detection accuracy achieved in real-world robot experiments

Self-calibration improves sensitivity to environmental variations

Effective detection of anomalies and foreign objects in unknown settings

Abstract

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