Application and Optimization of Computer Vision and Deep Learning in Road and Bridge Structure Monitoring

Traditional methods often rely on manual inspection and evaluation, which is time-consuming and laborious, not only inefficient, but also prone to subjective errors. Computer vision technology was utilized to improve the accuracy and precision of road and bridge structure monitoring, reduce human errors and achieve more reliable monitoring results. Convolutional Neural Network (CNN) was used to train and optimize large-scale data, achieving real-time monitoring and diagnosis of road and bridge structural states. Computer vision technology was applied to detect and identify road and bridge structural problems, such as automatically identifying cracks, damage, or deformation. The average time for monitoring road surface settlement of 5 road and bridge structures using computer vision and deep learning was 63.6% less than that of manually monitoring 5 road and bridge structures, indicating that the efficiency of monitoring road and bridge structures using computer vision and deep learning was higher.