MSAN-Net: An End-to-End Multi-Scale Attention Network for Universal Industrial Defect Detection

With the rapid advancement of automation and intelligence in the electronics manufacturing industry, the throughput of a single production line was grown exponentially. Although high efficiency brought significant cost and time advantages, it also led to two major challenges: (1) extremely low tolerance for error—any slight defect might have caused the entire product to be scrapped; (2) increasingly diverse and more concealed types of defects—bubble defects, internal chip defects, printed circuit board (PCB) defects, and specific process defects were continuously emerged, posing significant challenges to the inspection process. Traditional manual visual inspection or single-task deep learning models were often struggled to balance detection efficiency and accuracy in complex industrial scenarios. To address the above challenges, a single-stage industrial defect detection model based on multi-dataset mixed training—MSAN-Net—was proposed in this paper. Representative datasets covering the typical scenarios mentioned above were collected and organized, and part of the data was re-annotated to ensure a high level of consistency with actual production environments. MSAN-Net was adopted an integrated architecture, deeply combining UnifiedViT, C2f modules, convolution operations, SPPF structure, and Bi-Level Routing Attention mechanism to achieve accurate identification of complex industrial defects. Extensive experiments (including comparisons with multiple methods, ablation studies, and external validation) showed that MSAN-Net was outperformed existing SOTA models in industrial defect detection tasks, significantly improving detection accuracy for multi-class defects in complex scenarios, reducing reliance on manual inspection, and effectively lowering scrap losses caused by defects, thus providing a reliable solution for intelligent quality inspection in the electronics manufacturing industry.