Reliable Anomaly Detection and Localization System: Implications on Manufacturing Industry

Abstract

Industry 4.0 has placed significant emphasis on interconnectivity, digitalization, and automation. Among the myriad innovative technologies that have surfaced, artificial intelligence (AI) stands out as especially instrumental in the development of fully autonomous factories. Product quality inspection is a critical component of industrial manufacturing. An accurate and reliable AI-based Anomaly Detection and Localization (ADL) system for industrial product quality inspection is essential in real-world manufacturing factories. Collecting massive anomalous products is difficult because the number of anomalous products is limited and rare in a realistic manufacturing scenario. Therefore, the paper treats the ADL problem as a cold-start challenge, training the defects inspection network only using nominal (non-defective) images. Significantly, the paper aims to bridge the gap between academic research and real-world manufacturing industry applications. The paper lists issues that current state-of-the-art academic research faces when applied in real-world manufacturing settings, then a Reliable Anomaly Detection and Localization (RADL) system is developed to solve the issues. RADL is improved in three aspects. Firstly, the common image pre-processing method is modified by considering the characteristics of real-world industrial images. Secondly, a Fake Defect Feature Augmentation (FDFA) strategy to mitigate the scarcity of real-world data. Thirdly, a Hardness-aware Cross-Entropy loss (HCELoss) is adopted to enhance the stability and reliability of the system. On the public MVTec AD benchmarks, the proposed RADL outperforms previous methods with 99.53% in I-AUROC, 97.85% in P-AUROC, and 91.60% in PRO. Furthermore, RADL is evaluated under industrial manufacturing settings in two real-world datasets collected from industrial production lines. The experimental results demonstrate the superiority of the proposed strategies in a public dataset and real-world manufacturing industrial environments.

Keywords

• Industry 40
• anomaly detection
• manufacturing industry
• system reliability
• autonomous factory