An Active Learning Approach for Detecting Customer Induced Damages in Motherboards with Deep Neural Networks

Abstract

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 a Printed Circuit Board (PCB). In such cases, damaged units are not covered by warranty. The inspection of CIDs is usually performed by humans which may be costly and error prone. Modern computer vision techniques for object detection using deep neural networks can automatically and accurately detect CIDs on PCBs. The training of such networks requires a large labeled dataset of image examples of CIDs. Daily, hardware factories and repair centers generate hundreds of unlabeled images. Labeling them manually is laborious and time-consuming. Therefore, it is crucial to label the minimum amount of images such that the trained neural network can achieve comparable accuracy as if it were trained with the whole dataset. To this end, we propose an active learning approach that selects the most informative images for the object detector. For that, our approach is based on the uncertainty of the object detector, i.e., it selects new images based on class probability distribution given by the object detector. Also, we tackle some challenges that are intrinsic to this problem: i) it is a multiclass object detection problem since there are many types of defects; ii) there is a class accuracy imbalance; iii) there is a focus on recall, e.g. false positives are less harmful than false negatives, and iv) there are many images with no object which should not be selected for labeling. We evaluate this approach by using it to iteratively sample data, train and evaluate a model, and compare it with randomly sampled data. The results show that our method consistently outperforms random sampling by an average margin of 21.6%, proving to be a viable alternative for reducing the labeling cost and increasing detection accuracy in this domain.