  Video anomaly detection (VAD) is a challenging task aiming to recognize
anomalies in video frames, and existing large-scale VAD researches primarily
focus on road traffic and human activity scenes. In industrial scenes, there
are often a variety of unpredictable anomalies, and the VAD method can play a
significant role in these scenarios. However, there is a lack of applicable
datasets and methods specifically tailored for industrial production scenarios
due to concerns regarding privacy and security. To bridge this gap, we propose
a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The
industrial processes in our dataset are chosen through on-site factory research
and discussions with engineers. This dataset covers 16 different industrial
devices and contains over 6 hours of both synthetic and real-world video
footage. Moreover, we annotate the key feature of the industrial process, ie,
periodicity. Based on the proposed dataset, we introduce a period memory module
and a sliding window inspection mechanism to effectively investigate the
periodic information in a basic reconstruction model. Our framework leverages
LoRA adapter to explore the effective migration of pretrained models, which are
initially trained using synthetic data, into real-world scenarios. Our proposed
dataset and method will fill the gap in the field of industrial video anomaly
detection and drive the process of video understanding tasks as well as smart
factory deployment.
