提出轻量级异常检测模型，让边缘设备高效运行视觉异常检测。
PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge
- 用轻量网络降低内存与计算开销，适配边缘设备部署。
- 新算法PaSTe将推理时间减少25%，训练时间降33%，内存降76%。
- 适合需要低延迟、低功耗的工业质检、安防监控场景。
视觉异常检测（VAD）因其无需标注数据即可识别异常图像并定位异常区域而受到广泛关注。然而，现有研究对资源高效的VAD关注不足，尤其缺乏在边缘设备上的部署方案。本文采用轻量神经网络降低内存与计算需求，实现边缘端VAD可行性验证，并基于MVTec数据集对主流VAD算法进行基准测试。针对现有学生-教师特征金字塔匹配（STFPM）方法资源消耗高的问题，提出新型部分共享师生结构（PaSTe）。实验表明，PaSTe将推理时间减少25%，训练时间降低33%，训练峰值内存使用量下降76%。这些改进显著提升了VAD效率，为边缘设备实际部署奠定基础。
原文摘要 · Abstract (English)
Visual Anomaly Detection (VAD) has gained significant research attention for its ability to identify anomalous images and pinpoint the specific areas responsible for the anomaly. A key advantage of VAD is its unsupervised nature, which eliminates the need for costly and time-consuming labeled data collection. However, despite its potential for real-world applications, the literature has given limited focus to resource-efficient VAD, particularly for deployment on edge devices. This work addresses this gap by leveraging lightweight neural networks to reduce memory and computation requirements, enabling VAD deployment on resource-constrained edge devices. We benchmark the major VAD algorithms within this framework and demonstrate the feasibility of edge-based VAD using the well-known MVTec dataset. Furthermore, we introduce a novel algorithm, Partially Shared Teacher-student (PaSTe), designed to address the high resource demands of the existing Student Teacher Feature Pyramid Matching (STFPM) approach. Our results show that PaSTe decreases the inference time by 25%, while reducing the training time by 33% and peak RAM usage during training by 76%. These improvements make the VAD process significantly more efficient, laying a solid foundation for real-world deployment on edge devices.
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