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 c...

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Bibliographic Details
Main Authors Kompanets, Andrii, Pai, Gautam, Duits, Remco, Leonetti, Davide, Snijder, Bert
Format Journal Article
LanguageEnglish
Published 26.03.2024
Subjects
Computer Science - Computer Vision and Pattern Recognition
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Summary: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.
DOI:10.48550/arxiv.2403.17725