RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations
Recent advancements in industrial anomaly detection have been hindered by the lack of realistic datasets that accurately represent real-world conditions. Existing algorithms are often developed and evaluated using idealized datasets, which deviate significantly from real-life scenarios characterized by environmental noise and data corruption such as fluctuating lighting conditions, variable object poses, and unstable camera positions. To address this gap, we introduce the Realistic Anomaly Detection (RAD) dataset, the first multi-view RGB-based anomaly detection dataset specifically collected using a real robot arm, providing unique and realistic data scenarios. RAD comprises 4765 images across 13 categories and 4 defect types, collected from more than 50 viewpoints, providing a comprehensive and realistic benchmark. This multi-viewpoint setup mirrors real-world conditions where anomalies may not be detectable from every perspective. Moreover, by sampling varying numbers of views, the algorithm's performance can be comprehensively evaluated across different viewpoints. This approach enhances the thoroughness of performance assessment and helps improve the algorithm's robustness. Besides, to support 3D multi-view reconstruction algorithms, we propose a data augmentation method to improve the accuracy of pose estimation and facilitate the reconstruction of 3D point clouds. We systematically evaluate state-of-the-art RGB-based and point cloud-based models using RAD, identifying limitations and future research directions. The code and dataset could found at https://github.com/kaichen-z/RAD
Code (1)
Tasks
Anomaly DetectionData AugmentationPose EstimationSimilar Papers 제목 키워드 기반
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights
Anomaly detection is of paramount importance in many real-world domains, characterized by evolving behavior. Lifelong learning represents an emerging trend, answering the need for machine learning models that continuousl…
Anomaly DetectionContinual LearningLifelong learningEuropean Space Agency Benchmark for Anomaly Detection in Satellite Telemetry
Machine learning has vast potential to improve anomaly detection in satellite telemetry which is a crucial task for spacecraft operations. This potential is currently hampered by a lack of comprehensible benchmarks for m…
Anomaly DetectionTime SeriesTime Series Anomaly DetectionBINet: Multi-perspective Business Process Anomaly Classification
In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspectiv…
Anomaly ClassificationAnomaly DetectionAttributeClassification+1OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User
Time series anomaly detection is a vital task in many domains, including patient monitoring in healthcare, forecasting in finance, and predictive maintenance in energy industries. This has led to a proliferation of anoma…
Anomaly DetectionBenchmarkingTime SeriesTime Series Anomaly DetectionDirectional anomaly detection
Semi-supervised anomaly detection is based on the principle that potential anomalies are those records that look different from normal training data. However, in some cases we are specifically interested in anomalies tha…
Anomaly DetectionAttributeSemi-supervised Anomaly DetectionSupervised Anomaly Detection