At this stage of industrial production line, many types of circuit board soldering can not be automated instruments. In order to reduce the loss of manpower and material resources due to rework in factories for the phenomenon of missing solder in manual soldering of SMD components, automatic detection of soldering of SMD components by machine vision technology is proposed. Using the improved ResNet-FPN structure, the shallow feature information is fused with multi-scale channels, thus increasing the richness of feature information of tiny and occluded targets, reducing the training parameters, and speeding up the forward speed of network training; the number of classification samples is balanced and the loss value is reduced by introducing the Focal loss (FL). The experimental results show that the improved Cascade RCNN algorithm trains slightly faster than the original model, with a small increase in recall and an average mean accuracy (mAP) of 90.9%, which is 2.2 percentage points higher than the original model, and achieves better detection results.