A novel approach for industrial concrete defect identification based on image processing and deep convolutional neural networks

The preservation of structural integrity and durability is essential for the long-term viability of civil infrastructure projects. Addressing concrete defects promptly is crucial to achieving this objective. In this research, the research proposes a novel method for concrete defect analysis, harnessing the potential of image processing and deep learning techniques. It employs a fusionbased deep convolutional neural network (CNN), amalgamating the features of Inception V3, VGG16, and AlexNet architectures to identify and classify six distinct concrete defect characteristics, namely Cracks, Crazing, Efflorescence, Pop-out, Scaling, and Surface Cracks. Through rigorous training and validation, we thoroughly assess the performance of the proposed fusionbased CNN model. The testing phase reveals precision rates, with Crazing showing the lowest precision at 95%, and Cracks/Pop-outs achieving 98%, while other defect classifications exhibit exceptional 100% precision rates. The overall efficacy of our proposed model is evaluated using accuracy and F1-score metrics. Our findings demonstrate an impressive overall accuracy of 98.31% and an F1-score of 0.98, affirming the robustness and reliability of our approach. The outcomes of this study signify a significant advancement toward accurate and automated detection and classification of concrete defects.