Research on automatic detection and analysis of concrete cracks based on deep convolutional neural network architecture

As the aging of concrete structures becomes increasingly severe, crack detection has become a crucial aspect of maintaining their sustainable use. This paper investigates an automatic detection and analysis method for concrete cracks based on deep convolutional neural network architecture. The study utilized drones equipped with high-definition cameras to capture images of concrete structures such as bridges and roads, collecting over 10,000 original images containing cracks. Using Labelme software for point-by-point image annotation and applying data augmentation techniques like rotation and scaling, the dataset was expanded to 30,000 images to meet the requirements of deep learning model training. An improved AlexNet model was developed, replacing the fully connected layer with global average pooling to enhance robustness and reduce overfitting. The model achieved an average accuracy of 95.42% on the test set, outperforming traditional methods by 15%. The model also proved effective in noisy environments. Additionally, a semantic segmentation model based on Fully Convolutional Network (FCN) was introduced, incorporating atrous convolution and spatial pyramid pooling, achieving a pixel-level accuracy of 97.6%, surpassing benchmark models such as FCN and U-Net. The model accurately estimated crack width, with an error rate within ±2% compared to field measurements. This method improves detection efficiency and accuracy while reducing manual intervention, providing strong support for the maintenance of concrete structures.