Anomaly Detection Based on Deep Learning Using Video for Prevention of Industrial Accidents
Satoshi Hashimoto, Yonghoon Ji, Kenichi Kudo, Takayuki Takahashi, and, Kazunori Umeda
TL;DR
This paper introduces a deep learning-based video anomaly detection method aimed at preventing industrial accidents by identifying unusual events in industrial environments.
Contribution
It presents a novel application of deep learning for real-time anomaly detection in industrial videos to enhance safety measures.
Findings
Effective detection of anomalies in industrial videos
Potential to reduce industrial accidents
Improved safety monitoring capabilities
Abstract
This paper proposes an anomaly detection method for the prevention of industrial accidents using machine learning technology.
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Network Security and Intrusion Detection · Fire Detection and Safety Systems