  Recognition of defects in concrete infrastructure, especially in bridges, is
a costly and time consuming crucial first step in the assessment of the
structural integrity. Large variation in appearance of the concrete material,
changing illumination and weather conditions, a variety of possible surface
markings as well as the possibility for different types of defects to overlap,
make it a challenging real-world task. In this work we introduce the novel
COncrete DEfect BRidge IMage dataset (CODEBRIM) for multi-target classification
of five commonly appearing concrete defects. We investigate and compare two
reinforcement learning based meta-learning approaches, MetaQNN and efficient
neural architecture search, to find suitable convolutional neural network
architectures for this challenging multi-class multi-target task. We show that
learned architectures have fewer overall parameters in addition to yielding
better multi-target accuracy in comparison to popular neural architectures from
the literature evaluated in the context of our application.
