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.

翻译：视频异常检测（VAD）是一项具有挑战性的任务，旨在识别视频帧中的异常，现有的大规模VAD研究主要集中在道路交通和人类活动场景。在工业场景中，常存在多种不可预测的异常，VAD方法可在这些场景中发挥重要作用。然而，由于隐私和安全方面的考虑，目前缺乏专门针对工业生产场景的适用数据集和方法。为填补这一空白，我们提出专用于工业场景VAD的新数据集IPAD。该数据集的工业过程通过现场工厂调研以及与工程师讨论确定。该数据集涵盖16种不同的工业设备，包含超过6小时的合成及真实世界视频素材。此外，我们标注了工业过程的关键特征——周期性。基于所提出的数据集，我们引入周期记忆模块和滑动窗口检测机制，以有效探究基础重建模型中的周期信息。我们的框架利用LoRA适配器探索从合成数据初始训练的预训练模型向真实场景的有效迁移。所提出的数据集和方法将填补工业视频异常检测领域的空白，并推动视频理解任务及智能工厂部署的进程。