Hybrid-Segmentor: Hybrid approach for automated fine-grained crack segmentation in civil infrastructure
June Moh Goo, Xenios Milidonis, Alessandro Artusi, J. Boehm, Carlo Ciliberto
Abstract
It is essential to detect and segment cracks in various infrastructures, such as roads and buildings, to ensure safety, longevity, and cost-effective maintenance. Despite deep learning advancements, precise crack detection across diverse conditions remains challenging. This paper introduces Hybrid-Segmentor, a deep learning model combining Convolutional Neural Networks-based and Transformer-based architectures to extract both fine-grained local features and global crack patterns, significantly enhancing crack detection for improved infrastructure maintenance. Hybrid-Segmentor, trained on a large custom dataset created by merging multiple open-source datasets, can accurately detect cracks on different types of surfaces, crack shapes, and sizes. The model demonstrates robustness and versatility by accurately detecting discontinuities, vague cracks, non-crack regions within crack areas, blurred images, and complex crack contours. Furthermore, when compared against other recent models for crack segmentation, the proposed model achieves state-of-the-art performance, significantly outperforming them across five key metrics: accuracy (0.971), precision (0.807), recall (0.756), F1-score (0.774), and IoU (0.631).