LDINet: a lightweight defect inspection network for printed circuit boards combining attention mechanism and cross-scale fusion strategy

Abstract Deep learning models have been developed and applied to the field of printed circuit boards (PCBs) surface defect detection. However, there are still limitations that prevent the model from effectively balancing detection accuracy and model complexity. To address this issue, a lightweight defect inspection network is proposed to achieve accurate and efficient detection with reduced model parameters. Firstly, to improve the ability to express tiny defects on the PCB surface, an improved MobileNetV3, based on a lightweight and efficient channel attention network (ECANet), is adopted as the model backbone to achieve more efficient feature extraction with fewer parameters. Subsequently, to utilize both the deep semantic feature and the shallow fine-grained feature that the backbone component extracted and enhance the detection capability with notable scale changes, the novel cross-scale feature fusion module (ICCFM) based on combining reparameterization and ECANet attention mechanism is designed, which can effectively integrate context information and detail features, and integrate more abundant small defect features. Finally, comparative experiments on public and industrial datasets demonstrate that the proposed model is better suited for deployment in PCB production line inspection devices with limited computing resources.