CrackVisionX: A Fine-Tuned Framework for Efficient Binary Concrete Crack Detection

Cracks are critical defects in concrete structures, traditionally identified through human inspection. However, computer vision techniques, especially convolutional neural networks (CNNs), offer promising solutions for automated detection. Driven by this trend, this study proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrackVisionX</i>, a state-of-the-art deep learning framework for classifying binary concrete cracks. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrackVisionX</i> lies in its integration of advanced CNN architectures, ResNet50, MobileNet_v3_large, DenseNet121, and EfficientNetB0, with extensive hyper-parameter tuning. This integration optimizes crack detection accuracy while maintaining low model complexity and reducing bias, making it suitable for real-time applications. Furthermore, the framework introduces a robust data augmentation strategy that effectively addresses dataset imbalances, enhancing model generalization across diverse domains. Additionally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrackVisionX</i> employs comprehensive preprocessing on the METU and SDNET2018 datasets to create six domains: Bridge Deck, Wall, Pavement, SDNET2018, METU, and METU & SDNET2018. The framework’s performance is thoroughly evaluated and benchmarked against state-of-the-art methods, utilizing diverse metrics to improve the detection of cracks in concrete structures. EfficientNetB0, a core component of the framework, demonstrated superior performance with exceptional test accuracies of up to 99.71%, 99.78%, 99.55%, 99.89%, 99.98%, and 99.92% for Bridge Deck, Wall, Pavement, SDNET2018, METU, and METU & SDNET2018, respectively. Moreover, we evaluated the robustness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CrackVisionX</i> using images contaminated with different types and intensities of noise, demonstrating its reliability and effectiveness. This balance between high accuracy and computational efficiency confirms the framework’s potential for practical deployment. The experimental results emphasize the transformative potential of deep learning in construction safety and structural health monitoring.