PCB-Net: An Effective Deep Learning-Based Approach to PCBA Detection

Modern surface mount circuit board assemblies require more advanced defect detection methods. While deep learning algorithms have great potential for PCBA inspection, their detection accuracy in complex background situations is still limited. To overcome this problem, we propose a deep learning-based PCBA detection model (PCB-Net) to achieve accurate classification and localization of components on PCB. Firstly, for similar objects under complex background interference, this paper proposes a backbone network consisting of a generalized efficient aggregation network and a context converter to effectively extract local and global information. This integration aims to enhance the expressiveness of the network. Secondly, a multi-scale attention mechanism is designed to improve the feature extraction ability of the network on the target and suppress the interference of complex backgrounds. Finally, a C2fHB lightweight module was designed to improve the model's extraction of component features using the HorNet structure. Experimental results show that our proposed model is an effective PCBA detection method, as it can accurately detect tiny components in complex backgrounds, efficiently obtain component class and location information, and remain detection efficiency.