KairosAD：一种用于嵌入式设备工业异常检测的基于SAM的模型
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices
AI总结：
针对工业异常检测模型难以部署在资源受限嵌入式设备的问题，提出基于MobileSAM的有监督方法KairosAD，在保持相当AUROC性能的同时大幅减少参数并加快推理，且已成功部署于实际生产线。
摘要：
在智能制造时代，异常检测已成为维护现代生产线质量控制的必要手段。然而，尽管许多现有模型表现出良好的性能，但它们通常体积过大、计算需求高，难以部署在资源受限的嵌入式设备上，而这些设备能够轻松安装在中小企业（SME）的生产线上。为了弥补这一差距，我们提出了KairosAD，这是一种新颖的有监督方法，利用Mobile Segment Anything Model（MobileSAM）的功能进行基于图像的异常检测。KairosAD在两个知名的工业异常检测数据集（即MVTec-AD和ViSA）上进行了评估。结果表明，与领先的最新技术（SOTA）模型相比，KairosAD所需的参数量减少了78%，推理时间快了4倍，同时保持了相当的AUROC性能。我们将KairosAD部署在两个嵌入式设备上，即NVIDIA Jetson NX和NVIDIA Jetson AGX。最后，KairosAD被成功安装并在维罗纳大学工业计算机工程实验室（ICE Lab）的真实生产线上进行了测试。代码可在https://github.com/intelligolabs/KairosAD获取。
英文摘要：
In the era of intelligent manufacturing, anomaly detection has become essential for maintaining quality control on modern production lines. However, while many existing models show promising performance, they are often too large, computationally demanding, and impractical to deploy on resource-constrained embedded devices that can be easily installed on the production lines of Small and Medium Enterprises (SMEs). To bridge this gap, we present KairosAD, a novel supervised approach that uses the power of the Mobile Segment Anything Model (MobileSAM) for image-based anomaly detection. KairosAD has been evaluated on the two well-known industrial anomaly detection datasets, i.e., MVTec-AD and ViSA. The results show that KairosAD requires 78% fewer parameters and boasts a 4x faster inference time compared to the leading state-of-the-art model, while maintaining comparable AUROC performance. We deployed KairosAD on two embedded devices, the NVIDIA Jetson NX, and the NVIDIA Jetson AGX. Finally, KairosAD was successfully installed and tested on the real production line of the Industrial Computer Engineering Laboratory (ICE Lab) at the University of Verona. The code is available at https://github.com/intelligolabs/KairosAD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
