MN-Net: Multi-Scale Feature Fusion and Neighborhood Attention Self-Supervised Network for Industrial Spool Surface Anomaly Detection

As a key component in industrial production, industrial spools are critical for ensuring production stability and personnel safety, primarily relying on supervised learning for anomaly detection. Although supervised learning methods achieve high detection accuracy, they depend heavily on numerous manual annotations. Therefore, self-supervised learning methods emerge as potential solutions. However, traditional self-supervised methods often overly rely on the reconstruction capabilities of sub-networks when dealing with anomalous images, leading to unsatisfactory reconstruction accuracy and poor detection results. To address these issues, we propose a self-supervised anomaly detection method for industrial spool surfaces, called MN-Net. This method adapts to complex anomaly detection tasks in various industrial scenarios by using automatically generated pseudo-labels for training, eliminating the need for manual annotation. To handle interference from synthetic anomaly information caused by different feature scales, we introduce a Multi-Scale Feature Fusion (MFF) module. Additionally, to enhance the model's ability to identify anomalies, we incorporate the Neighborhood Attention (NAM) module, which significantly improves anomaly detection by focusing on local anomalies. To evaluate the detection accuracy of MN-Net, we conducted extensive experimental studies on the industrial spool dataset and the BSData dataset. The results demonstrate that MN-Net outperforms existing methods, achieving image-level AUROC (I-AUROC) scores of 96.0% and 93.8%, and pixel-based AUROC (P-AUROC) scores of 95.3% and 90.6% on the industrial spool dataset and BSData dataset, respectively.