基于RetinaNet的手机主板缺陷检测研究

Defect detection of mobile phone motherboard based on RetinaNet

不同型号手机的主板图像具有多分辨率的成像模式,使缺陷元件是多尺度的。常规缺陷检测方法主要有图像融合方法和提取统计模型的方法,但这些方法的鲁棒性仍需要提高。针对该问题,提出了一种自动检测网络模型,即RetinaNet目标检测器。首先使用特征金字塔网络(FPN)提取缺陷元件的多尺度特征分类和位置,然后引入MobileNetV2以压缩和加速RetinaNet模型,最后使用焦点损失解决类不平衡和难以检测样本对损失贡献程度的问题。实验结果表明, RetinaNet能有效地检测不同尺度的缺陷元件,具有很高的检测精度;与其他目标检测器相比,RetinaNet实现了超过95%的平均精度(mAP)。这些结果表明了本文所提模型的有效性。

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.