  Recognising reinforced concrete defects (RCDs) is a crucial element for
determining the structural integrity, traffic safety and durability of bridges.
However, most of the existing datasets in the RCD domain are derived from a
small number of bridges acquired in specific camera poses, lighting conditions
and with fixed hardware. These limitations question the usability of models
trained on such open-source data in real-world scenarios. We address this
problem by testing such models on our "dacl1k" dataset, a highly diverse RCD
dataset for multi-label classification based on building inspections including
1,474 images. Thereby, we trained the models on different combinations of
open-source data (meta datasets) which were subsequently evaluated both
extrinsically and intrinsically. During extrinsic evaluation, we report metrics
on dacl1k and the meta datasets. The performance analysis on dacl1k shows
practical usability of the meta data, where the best model shows an Exact Match
Ratio of 32%. Additionally, we conduct an intrinsic evaluation by clustering
the bottleneck features of the best model derived from the extrinsic evaluation
in order to find out, if the model has learned distinguishing datasets or the
classes (RCDs) which is the aspired goal. The dacl1k dataset and our trained
models will be made publicly available, enabling researchers and practitioners
to put their models to the real-world test.
