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image sensory anomaly detection

Image sensory anomaly detection is a computer vision process focused on identifying and localizing subtle physical flaws, surface defects, or unexpected visual irregularities within images of specific objects or textures. Unlike semantic anomaly detection, which determines whether an entire image belongs to an unfamiliar class or category, sensory anomaly detection focuses on part-level or patch-level deviations from an established standard of normal appearance, such as scratches, cracks, contaminations, or structural deformities. This task is widely utilized in automated industrial quality control and medical diagnostics, where machine learning systems are typically trained primarily on defect-free images to learn nominal patterns and subsequently detect, score, and segment unforeseen defects during inspection.

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SoftPatch: Unsupervised Anomaly Detection with Noisy Data

SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Xi Jiang, Jianlin Liu, Jinbao Wang, Qiang Nie, Kai Wu, Yong Liu, Chengjie Wang, Feng Zheng

OrganizationsDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyTencent

Why you should read this

Proposes SoftPatch, a patch-level denoising and memory re-weighting method that prevents defective training samples from distorting decision boundaries in real-world unsupervised anomaly detection.

Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper considers label-level noise in image sensory anomaly detection for the first time. To solve this problem, we proposed a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset. Comprehensive experiments in various noise scenes demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the MVTecAD and BTAD benchmarks and is comparable to those methods under the setting without noise.

Added

2026-09-26