Unsupervised industrial image defect detection based on autoencoder and GANs

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUC: 93.6 ± 0.5%

• Primary Outcome: Defect detection accuracy measured by AUC of Recall-Precision curve

• Nnt Or Sensitivity: F1-Score: 0.890 ± 0.003 for scratches and dents; False alarm rate: 8.6 ± 0.7%

• Confidence Interval: Not reported

Clinical Application

Moderate - requires technical expertise and computational infrastructure but reduces manual labeling requirements Relevant to Australian manufacturing sector, particularly automotive and electronics industries. May align with Industry 4.0 initiatives and advanced manufacturing programs supported by government agencies Industrial manufacturing quality control systems, particularly in automotive, semiconductor, and electronics sectors

Abstract

In the process of intelligentization in modern manufacturing, especially in industrial fields such as automobile manufacturing, semiconductor production, and electronic product assembly, product quality control is crucial. Traditional defect detection methods face problems such as supervised learning methods relying on a large amount of labeled data, weak generalization ability, and high cost. In the process of intelligent manufacturing, industrial product quality control is a key link to ensure production safety and product consistency. Especially in typical industrial scenarios such as automobile manufacturing, semiconductor production, and electronic product assembly, traditional defect detection methods are difficult to meet the needs of actual production lines for high-precision and high-efficiency detection due to their reliance on a large amount of labeled data, weak generalization ability, and high cost. To solve the problem of defect detection in small and zero sample scenarios in these industries, improve detection sensitivity and localization accuracy, and enhance the model's generalization ability to unknown defects, a unsupervised industrial image defect detection method based on autoencoder and Generative Adversarial Networks (GANs) is proposed. This study constructs an Multi-level Deep feature Adaptive fusion AutoEncoder (MDAAE) module, extracts multi-scale features through Pre-Trained Convolutional Neural Backbone Networks (PTCNBN), introduces Attention Mechanism (AM) to dynamically calculate feature weights, and achieves feature fusion and reconstruction. Meanwhile, the self-AM is fused to improve the GANs. The self-attention module efficiently captures long-range dependencies and generates an adversarial network training objective function to optimize the generator's unsaturated loss. The results showed that the Area Under the Curve (AUC) of the Recall-Precision curve of the research method reached 93.6 ± 0.5%, and its F1-Score value exceeded 0.890 ± 0.003 in defect types such as scratches and dents. In practical applications, the inference time of the research method remained stable at 3.0 ± 0.2 GB of central processor memory, and the Fréchet Inception Distance (FID) value fully converged at the 2230th iteration, with a stable FID value of 3. The false alarm rate was only 8.6 ± 0.7% under strong light conditions. The proposed UIIDD method based on autoencoder and GANs had good robustness, generalization ability, reliability, and efficiency. This study effectively solves the problems of poor robustness, weak generalization ability, and high cost of traditional defect detection methods.

References

1. 1.An, S., Wu, J., & Li, J. (2026). Unsupervised industrial image defect detection based on autoencoder and GANs. PLoS One. https://doi.org/10.1371/journal.pone.0346637