用轻量版SAM模型实现嵌入式设备上的工业缺陷检测
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices
- 基于MobileSAM构建轻量化监督异常检测模型
- 参数减少78%，推理速度提升4倍，性能相当
- 已在真实产线部署验证，适合中小企业使用
智能制造时代，异常检测对保障产线质量至关重要。然而，现有模型通常体积庞大、计算开销高，难以在资源受限的嵌入式设备上部署，限制了中小型企业的应用。为此，我们提出KairosAD，一种基于Mobile Segment Anything Model（MobileSAM）的新型监督异常检测方法。该模型在MVTec-AD和ViSA两个经典工业异常检测数据集上进行了评估，结果显示，相比当前最先进的模型，KairosAD参数量减少78%，推理速度提升4倍，同时保持相当的AUROC性能。我们已将KairosAD成功部署于NVIDIA Jetson NX与Jetson AGX两款嵌入式设备，并在维罗纳大学工业计算机工程实验室（ICE Lab）的真实产线上完成安装与测试。代码已开源：https://github.com/intelligolabs/KairosAD。
原文摘要 · Abstract (English)
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.
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