IPAD: Industrial Process Anomaly Detection Dataset

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. Project page: https://ljf1113.github.io/IPAD_VAD/

视频异常检测（Video Anomaly Detection，VAD）是一项极具挑战性的任务，旨在识别视频帧中的异常样本。当前主流的大规模VAD研究主要聚焦于道路交通与人类活动场景。在工业场景中，往往存在多种难以预判的异常事件，VAD方法在此类场景中可发挥重要作用。然而，受隐私与安全因素制约，目前缺乏专门针对工业生产场景的适配数据集与对应方法。为填补这一空白，我们构建了专为工业场景VAD设计的全新数据集IPAD。本数据集收录的工业流程均通过实地工厂调研并与工程师研讨后选定，涵盖16种不同的工业设备，包含总时长超6小时的合成与真实世界视频素材。此外，我们还对工业流程的核心特征——周期性——进行了标注。基于该数据集，我们提出了周期记忆模块与滑动窗口检测机制，可在基础重构模型中有效挖掘周期性信息。我们的框架采用LoRA适配器（Low-Rank Adaptation），实现了以合成数据预训练的模型向真实场景的有效迁移。本数据集与所提方法将填补工业视频异常检测领域的空白，推动视频理解任务与智能工厂部署的发展进程。项目主页：https://ljf1113.github.io/IPAD_VAD/