An Innovative Dense ResU-Net Architecture With T-Max-Avg Pooling for Advanced Crack Detection in Concrete Structures

Abstract

Computer vision which uses Convolutional Neural Network (CNN) models is a robust and accurate tool for precise monitoring and pixel-level detection of potential damage in concrete structures. Using a state-of-the-art Dense ResU-Net model integrated with T-Max-Avg pooling layers, the present study introduces a novel and effective method for crack detection in concrete structures. The major innovation of this research is the introduction of the T-Max-Avg pooling layer within the Dense ResU-Net architecture which synergistically combines the strengths of both max and average pooling to improve feature retention and minimize information loss during crack detection. In addition, the incorporation of Residual and Dense blocks within the U-Net framework significantly enhances feature extraction and network depth, resulting in a more robust anomaly detection. The implementation of extensive data augmentation techniques improves the robustness of the model while the application of spatial dropout and L2 regularization techniques prevents overfitting. The proposed model showed a superior performance, outperforming traditional and state-of-the-art models. It had a Dice Coefficient score of 97.41%, an Intersection-over-Union (IoU) score of 98.63%, and an accuracy of 99.2% using a batch size of 32. These results confirmed the reliability and efficacy of the Dense ResU-Net with T-Max-Avg pooling layer for accurate crack detection, demonstrating its potential for real-world applications in structural health monitoring. By taking advantage of advanced deep learning techniques, the proposed method addressed the limitations of traditional crack detection techniques and offered significant improvements in robustness and accuracy.