Efficient Visual Anomaly Detection at the Edge: Enabling Real-Time Industrial Inspection on Resource-Constrained Devices

TL;DR

This paper introduces two efficient visual anomaly detection methods, PatchCore-Lite and Padim-Lite, optimized for resource-constrained edge devices to enable real-time industrial inspection with reduced memory and computation.

Contribution

The paper presents novel lightweight VAD algorithms tailored for edge deployment, significantly reducing memory and inference time compared to existing models.

Findings

PatchCore-Lite reduces memory footprint by 79%.

Padim-Lite decreases total memory by 77% and inference time by 31%.

Both methods are effective for real-time industrial inspection on edge devices.

Abstract

Visual Anomaly Detection (VAD) is essential for industrial quality control, enabling automatic defect detection in manufacturing. In real production lines, VAD systems must satisfy strict real-time and privacy requirements, necessitating a shift from cloud-based processing to local edge deployment. However, processing data locally on edge devices introduces new challenges because edge hardware has limited memory and computational resources. To overcome these limitations, we propose two efficient VAD methods designed for edge deployment: PatchCore-Lite and Padim-Lite, based on the popular PatchCore and PaDiM models. PatchCore-Lite runs first a coarse search on a product-quantized memory bank, then an exact search on a decoded subset. Padim-Lite is sped up using diagonal covariance, turning Mahalanobis distance into efficient element-wise computation. We evaluate our methods on the MVTec AD and VisA benchmarks and show their suitability for edge environments. PatchCore-Lite achieves a remarkable 79% reduction in total memory footprint, while PaDiM-Lite achieves substantial efficiency gains with a 77% reduction in total memory and a 31% decrease in inference time. These results show that VAD can be effectively deployed on edge devices, enabling real-time, private, and cost-efficient industrial inspection.