Deep Learning for Early Damage Detection of Tailing Pipes Joints with a Robotic Device

Filho, Levi Welington de Resende; Santos, André Almeida; Azpúrua, Héctor; Garcia, Gabriel; Pessin, Gustavo

doi:10.1109/case48305.2020.9216881

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Classification Of Anomalies In Arms1

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2024

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“…Resende et al [ 48 ] offer another example: any surface variation within a pipe transporting tailings from a plant to a dam was considered an anomaly. The anomalies were detected using a deep learning model that analysed camera images of the pipe collected by the robot.…”

Section: Classification Of Anomalies In Armsmentioning

confidence: 99%

Anomaly Detection Methods in Autonomous Robotic Missions

Chirayil Nandakumar,

Mitchell,

Erden

et al. 2024

Sensors

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Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.

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Section: Classification Of Anomalies In Armsmentioning

confidence: 99%