Work

ObjSegAD-Net: Region-aware pseudo-defect injection and dual-branch architecture for unsupervised industrial anomaly detection

Year: 2025

Type: article

Abstract: Anomaly detection in industrial manufacturing identifies product defects. However, limited anomalous samples and background noise often reduce accuracy and increase false alarms. To solve this, we propose ObjSegAD-Net, an unsupervised anomaly detection method. It separates each image into foreground and background regions. It uses foreground-guided pseudo-defect synthesis to inject diverse synthetic anomalies, boosting data diversity with more pseudo-samples. For the background, it adds noise such as Gaussian blur, helping the model distinguish true defects from irrelevant variations. During inference, ObjSegAD-Net adopts a dual-branch architecture with region-aware attention mechanisms, which adaptively enhances responses to foreground anomalies while suppressing background interference. This design significantly reduces false positives and improves detection accuracy under complex noisy conditions. ObjSegAD-Net achieves state-of-the-art results on multiple industrial anomaly detection benchmarks, demonstrating the robustness and generalization capabilities of its region-aware pseudo-defect generation and dual-task architecture.

Source: Information Fusion

DOI: 10.1016/j.inffus.2025.103707

Authors: Shaohua Dong, Fangxu Hu, Bing Wei, Yi Wu

Institution: Hainan University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,740