Aging civil infrastructures are closely monitored by engineers for damage and
critical defects. As the manual inspection of such large structures is costly
and time-consuming, we are working towards fully automating the visual
inspections to support the prioritization of maintenance activities. To that
end we combine recent advances in drone technology and deep learning.
Unfortunately, annotation costs are incredibly high as our proprietary civil
engineering dataset must be annotated by highly trained engineers. Active
learning is, therefore, a valuable tool to optimize the trade-off between model
performance and annotation costs. Our use-case differs from the classical
active learning setting as our dataset suffers from heavy class imbalance and
consists of a much larger already labeled data pool than other active learning
research. We present a novel method capable of operating in this challenging
setting by replacing the traditional active learning acquisition function with
an auxiliary binary discriminator. We experimentally show that our novel method
outperforms the best-performing traditional active learning method (BALD) by 5%
and 38% accuracy on CIFAR-10 and our proprietary dataset respectively.

通过利用无人机技术和深度学习，希望实现老化土木基础设施的自动化视觉检测，同时提出了一种针对传统主动学习设置的新方法，用于代替传统的主动学习获取函数，实验证明其在性能和准确性方面均优于现有的方法。