Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges

Abstract

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.

References in corpus (4)

• Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
• Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement
• Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation
• FPCNet: Fast Pavement Crack Detection Network Based on Encoder-Decoder Architecture