Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges
<|reference_start|>Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges: 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.<|reference_end|>
@article{kompanets2024deep,
    title={Deep Learning for Segmentation of Cracks in High-Resolution Images of
  Steel Bridges},
    author={Andrii Kompanets, Gautam Pai, Remco Duits, Davide Leonetti, Bert
  Snijder},
    journal={arXiv preprint arXiv:2403.17725},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17725},
    primaryClass={cs.CV eess.IV}
}