Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network

Automated quality control of pavement and concrete surfaces is essential for maintaining structural integrity and consistency in the construction and infrastructure industries. This paper presents a novel deep learning model designed for automated quality control of these surfaces during both construction and maintenance phases. The model employs per-pixel segmentation and per-image classification, integrating both local and broader context information. Additionally, we utilize the classification results to improve segmentation during both training and inference stages. We evaluated the proposed model on a publicly available dataset containing more than 7,000 images of pavement and concrete cracks. The model achieved a Dice score of 81% and an intersection-over-union of 71%, surpassing publicly available state-of-the-art methods by at least 6-7 percentage points. An ablation study confirms that leveraging classification information enhances overall segmentation performance. Furthermore, our model is computationally efficient, processing over 30 FPS for 512x512 images, making it suitable for real-time applications on medium-resolution images. Upon acceptance, both the code and the corrected dataset ground truths will be made publicly available.