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.

利用无人机和图像处理技术自动化当前桥梁视觉检查实践是提高检查效果、鲁棒性和降低成本的一种显著途径。本文研究了一种新颖的深度学习方法，用于检测高分辨率钢桥图像中的疲劳裂纹，首先，我们介绍了一个包含钢桥裂纹图像的新颖而具有挑战性的数据集。其次，我们将 ConvNext 神经网络与先前最先进的编码器 - 解码器网络集成，进行裂纹分割研究和报告，并研究了在应用于高分辨率钢桥裂纹图像时使用背景补丁对网络性能的影响。最后，我们引入了一种损失函数，允许在训练过程中使用更多的背景补丁，从而显著降低了误报率。