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.

翻译：摘要：利用无人机和图像处理技术实现现有桥梁目视检查过程的自动化，是使这些检查更高效、更稳健且成本更低的重要途径。本文研究了一种新颖的深度学习方法，用于检测钢桥高分辨率图像中的疲劳裂缝。首先，我们提出了一个包含钢桥裂缝图像的新颖且具有挑战性的数据集。其次，将ConvNext神经网络与先前最先进的编码器-解码器网络进行集成，用于裂缝分割。我们研究并报告了背景补丁的使用对网络在钢桥高分辨率裂缝图像上性能的影响。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背景补丁，从而显著降低假阳性率。