Since the motherboard images of different mobile phones have a multi-resolution imaging mode, the shape of defective components become multi-scale. Conventional defect detection methods mainly include image fusion methods or statistical model extraction methods, but the robustness of these methods still needs to be improved. To solve this problem, an automatic learning representation model, called RetinaNet object detector, is proposed. Firstly, feature pyramid network (FPN) is used to extract the multi-scale feature classification and location of defective components, and MobileNetV2 is introduced to compress and accelerate the RetinaNet model. Secondly, focus loss is used to resolve the class imbalance and increase the difficulty of detecting the contribution of the samples to the loss during the training. The experimental results show that RetinaNet can effectively detect defective components of different scales, and has high detection accuracy. Compared with other object detectors, RetinaNet achieves an average accuracy (mAP) of over 95%. These results demonstrate the effectiveness of the proposed model.