Efficient Lightweight CNN and 2D Visualization for Concrete Crack Detection in Bridges

The durability and safety of modern concrete architecture and infrastructure are critically impacted by early-stage surface cracks. Timely and appropriate identification and management of these cracks are therefore essential to enhance structural longevity and stability. This study utilizes computer vision technology to construct a large-scale database, comprising 106,998 concrete surface crack images from various research sources. Through data augmentation, the database is extended to 140,000 images to fully leverage the advantages of deep learning models. For concrete surface crack detection, this study proposed a lightweight convolutional neural network (CNN) model, achieving 92.27% accuracy, 94.98% recall, and a 92.39% F1 score. Notably, the model runs smoothly on lightweight office notebooks without GPUs. Additionally, an image stitching algorithm that seamlessly stitches multiple images was proposed to generate high-quality panoramic views of bridges. The image stitching algorithm demonstrates robustness when applied to multiple images, successfully achieving stitching without visible seams or errors, providing efficient and reliable technical support for bridge panorama generation. The research outcomes demonstrate significant practical value in bridge inspection, providing robust technical support for safe and efficient bridge inspection. Moreover, our findings offer valuable references for future research and applications in related fields.