Efficient Detection and Measurements of Bridge Crack Widths Based on Streamlined Convolutional Neural Network

The automation of bridge disease detection necessitates the time-consuming, labor-intensive manual detection process and the limitations of traditional image segmentation methods, such as inadequate denoising effects and insufficient continuity in crack segmentation. This paper proposes a rapid detection and information feedback approach based on an enhanced Convolutional Neural Network (CNN) model to tackle these issues in bridge crack width measurement and information processing. To improve efficiency and accuracy in bridge safety monitoring, the training data is constructed by the bridge image library and network crack through the refined preprocessing and image segmentation techniques applied to these images, key features of cracks are identified and extracted to enhance the capability for crack identification. For crack assessment, the maximum internal tangent circle method is employed to accurately measure the width of bridge abutment cracks. The effectiveness of our model was verified through both fixed-point detection and Unmanned Aerial Vehicle (UAV) dynamic detection, ensuring comprehensive and accurate data collection. This dual validation strategy shows that our model substantiates the wide applicability across various scenarios, and the non-contact crack measurement technique achieves a precision of 0.01 mm, demonstrating the effectiveness and accuracy of this streamlined CNN model in accurately assessing crack width.