Exploring EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies: A Brief Overview

Author(s): Vincent Liu

Originally published on Towards AI.

A Real-Time Anomaly Detection Network surpasses all the existing networks
Source: Image by falco from Pixabay

Anomaly Detection is one of the critical applications in the manufacturing industry. It boosts productivity and saves costs. The number of samples to be processed by the computer within a period in manufacturing is way greater than that in other industries.

Anomaly Detection in computer vision has come a long way and many fantastic algorithms are deployed in production. However, the community always seeks a more efficient solution without sacrificing accuracy.

In this article, I would like to share a disruptive breakthrough in anomaly detection published in Feb. 2024, EfficentAD. It is an unsupervised learning approach. The network is only trained with anomaly-free application images from certain domains. For example, if we want a model able to detect stains on paper, we train the model with all the stain-free paper images. The anomalous images with stains on the paper are only applied in model evaluation.

Figure 1. Performance comparison with existing networks. Source: original paper¹

Below are three points that make EfficentAD stand out

EfficientAD surpasses the existing works both in accuracy and efficiency. The latency of processing one image drops drastically from over 10ms to 2ms.EfficientAD is the first approach in anomaly detection that hits over 95% accuracy in… Read the full blog for free on Medium.

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Published via Towards AI