Patch Memory Bank k-NN for Semi-supervised Visual Hazard Detection in Indoor Mobile Robots

Patch Memory Bank k-NN for Semi-supervised Visual Hazard Detection in Indoor Mobile Robots ------------**License**------------Dataset is available under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/. ------------**Summary**------------The project provides a semi-supervised anomaly detection framework for indoor mobile robots using global and patch-level features extracted from CNN and Vision Transformer backbones. Hazards are treated as visual anomalies compared to a memory bank of patch features collected from normal data. It is obligatory to cite the following paper in every work that uses the dataset: *Wozniak, P., Krzeszowski T.: Patch Memory Bank k-NN for Semi-supervised Visual Hazard Detection in Indoor Mobile Robots, Computational Science – ICCS 2026. ICCS 2026. Lecture Notes in Computer Science, Springer, Cham.* ------------**Data description**------------The dataset was specifically prepared for evaluation purposes, consisting of scenes recorded by a real mobile robot and including natural variability due to environmental changes. To create controlled evaluation scenarios, the dataset was extended with synthetically augmented data. Our approach integrates real-world and synthetically augmented data to enable systematic and robust assessment of~anomaly detection performance for mobile robots under diverse conditions. The dataset was created based on The Multi-Domain Dataset for Robots (MDDRobots) (https://doi.org/10.1038/s41597-025-05124-3). ------------**Dataset Structure**------------- DataSet_RobotPiCamera_RGB_train - training data from the MDDRobots dataset (https://doi.org/10.1038/s41597-025-05124-3)- DataSet_RobotPiCamera_RGB_hazards - test data with synthetically generated hazards- Code - implementation of the method described in the paper ------------**Further information**------------For any questions, comments or other issues please contact Piotr Woźniak <[e-mail]>.