CrackVision: Effective Concrete Crack Detection with Deep Learning and Transfer Learning

Crack evaluation is critical for assessing the structural integrity of concrete, significantly impacting safety, functionality, and durability. In response to the growing demand for more advanced crack detection techniques, this paper presents CrackVision, an advanced framework that leverages Transfer Learning (TL) to enhance traditional methods of concrete crack detection. The framework utilizes pre-trained Convolutional Neural Networks (CNNs), such as ResNet50, Xception, and InceptionV3, which are fine-tuned to address the unique challenges of concrete crack detection. Initially trained on extensive datasets, these models have been further enhanced to improve their accuracy and reliability. The effectiveness of these models is rigorously evaluated using two key datasets: METU and SDNET2018. Strategic resampling techniques were employed to address the data imbalance in the SDNET2018 dataset, thereby improving the effectiveness of CrackVision for both binary and multi-class classification tasks. The findings reveal that the ResNet50 model achieves remarkable accuracy, reaching 99.95% on the METU dataset and consistently exceeding 97% on the balanced SDNET2018 dataset. These results demonstrate the effectiveness of the CrackVision framework, particularly emphasizing the exceptional performance of the ResNet50 model through Transfer Learning in concrete crack detection.