Image defect classification of surface mount technology welding based on the improved ResNet model

In mass production, welding flaw detection in existing surface mount technology (SMT) has certain constraints, including its high costs, heavy workloads, and time-consuming processes. However, image classification technology using computer vision demonstrates high detection speeds and considerably reduced detection costs in flaw detection. Nevertheless, the increased integration of chip components on printed circuit boards (PCBs) and reduced component sizes pose challenges for flaw detection technology. Therefore, in this paper, an SMT welding image flaw classification model—that is, the ResNet-34-ECA model—based on an improved ResNet model, is proposed. Initially, the dataset is amplified using data amplification methods, such as stochastic rotation, increased data diversity, and enhanced model robustness. The ResNet34 model is then optimized using the light quantization efficient channel attention (ECA) module, resulting in higher classification accuracy. The experimental data in this study were collected using automated optical inspection (AOI) equipment, following the manual creation and amplification of the dataset. The experimental results showed that the baseline model accuracy increased by 0.22 in the augmented dataset, reaching 97.2%. Moreover, the ResNet-34-ECA model proposed in this paper could realize the classification of SMT welding image defects successfully; the overall classification accuracy of the improved ResNet image classification model was 0.01 higher than that of the baseline model, reaching 98.2%. Consequently, the proposed model proves to be better than other models in defect classification on this dataset, providing an accurate classification of SMT welding image defects.