Applications of Computer Vision in Anomaly Detection

In this article, we delve into the realm of computer vision and anomaly detection, exploring a novel approach to addressing logical AD (anomaly detection) challenges. Logical AD is crucial for accurately identifying abnormalities in visual data, yet existing methods struggle with differentiating similar components. To overcome this limitation, we propose a hybrid feature reconstruction model that considers global dependencies among multiple components. By semantically segmenting the product’s components, our method can precisely detect anomalies without relying on supervised guidance.
To begin with, let’s set the scene: imagine you have a basket of mixed fruits – some are ripe, while others are not. You want to identify which fruit is not ripened without cutting them all open. This is where our approach comes in – we use a combination of techniques to differentiate between the various components of the image, much like how you can distinguish between different fruits based on their colors and shapes.
Our method leverages both supervised and unsupervised learning techniques to improve anomaly detection. We utilize features from a pre-trained backbone network to capture semantic information and generate three different anomaly scores with varying scales and distributions. To effectively combine these scores, we employ adaptive scaling using statistics obtained from generative models. This approach ensures that the segmentation of normal samples is accurate, which in turn enables more precise anomaly detection.
Now, let’s dive deeper into the nitty-gritties: our proposed method builds upon the transductive approach (Boudiaf et al., 2021) by updating both the backbone network and the classifier with a histogram matching loss. This allows us to better utilize logical constraints shared across normal images, leading to improved anomaly detection performance. Additionally, we introduce an adversarial training strategy to enhance the robustness of our method against various attacks.
In conclusion, by leveraging both supervised and unsupervised learning techniques, we have proposed a novel approach to addressing logical AD challenges. Our hybrid feature reconstruction model offers improved accuracy and robustness in anomaly detection tasks, making it an exciting development in the field of computer vision.