  Automating the current bridge visual inspection practices using drones and
image processing techniques is a prominent way to make these inspections more
effective, robust, and less expensive. In this paper, we investigate the
development of a novel deep-learning method for the detection of fatigue cracks
in high-resolution images of steel bridges. First, we present a novel and
challenging dataset comprising of images of cracks in steel bridges. Secondly,
we integrate the ConvNext neural network with a previous state-of-the-art
encoder-decoder network for crack segmentation. We study and report, the
effects of the use of background patches on the network performance when
applied to high-resolution images of cracks in steel bridges. Finally, we
introduce a loss function that allows the use of more background patches for
the training process, which yields a significant reduction in false positive
rates.
