Detecting Customer Induced Damages in Motherboards with Deep Neural Networks

Identifying Customer Induced Damage (CID) is a key part in warranty programs of electronics manufacturers. CID is defined as any damage in the unit performed by an unauthorized person including the customer. In such cases, damaged units are not covered by warranty. An important aspect of CID inspection activities is that, in most industries, the task is performed manually. Since such task demands high attention to details, human mistakes occur very often. For such reasons, CID detection can be considered as a good candidate to be handled by automatic detection methods with computer vision tools. In this work, we evaluate modern computer vision methods based on deep neural networks (SwinSoft Teacher and Mask-RCNN as prediction networks with swin and resnet backbones) for automatic CID detection in motherboard images. All images were originated from private company repair centers around the globe. We highlight the following results for Average Precision (AP) and Average Recall (AR):
$AP{@}0.5 =\mathbf{80.2}$
and
$AR^{m}=\mathbf{57.9}$
, for Mask-RCNN with Swin-S backbone, where the variant
$m$
means medium, and
$AP{@}0.5 =\mathbf{74.2}$
and
$AR^{m}=\mathbf{45.4}$
, for Mask-RCNN Swin-T backbone. Our results demonstrate that Swin-based methods had the best overall results and exhibit interesting characteristics for our application.