An enhanced method of CNNs by incorporating the clustering-guided block for concrete crack recognition

Abstract Concrete cracking poses a significant threat to the safety and stability of crucial infrastructure such as bridges, roads, and building structures. Recognizing and accurately measuring the morphology of cracks is essential for assessing the structural integrity of these elements. This paper introduces a novel Crack Segmentation method known as CG-CNNs, which combines a Clustering-guided (CG) block with a Convolutional Neural Network (CNN). The innovative CG block operates by categorizing extracted image features into K groups, merging these features, and then simultaneously feeding the augmented features and original image into the CNN for precise crack image segmentation. It automatically determines the optimal K value by evaluating the Silhouette Coefficient for various K values, utilizing the grayscale feature value of each cluster centroid as a defining characteristic for each category. To bolster our approach, we curated a dataset of 2500 crack images from concrete structures, employing rigorous pre-processing and data augmentation techniques. We benchmarked our method against three prevalent CNN architectures: DeepLabV3 + , U-Net, and SegNet, each augmented with the CG block. An algorithm specialized for assessing crack edge recognition accuracy was employed to analyze the proposed method's performance. The comparative analysis demonstrated that CNNs enhanced with the CG block exhibited exceptional crack image recognition capabilities and enabled precise segmentation of crack edges. Further investigation revealed that the CG-DeepLabV3 + model excelled, achieving an F1 score of 0.90 and an impressive intersection over union (IoU) value of 0.82. Notably, the CG-DeepLabV3 + model significantly reduced the recognition error for locating crack edges to a mere 2.31 pixels. These enhancements mark a significant advancement in developing accurate algorithms based on deep neural networks for identifying concrete crack edges reliably. In conclusion, our CG-CNNs approach offers a highly accurate method for crack segmentation, which is invaluable for machine-based measurements of cracks on concrete surfaces.