融合改进ResNet-14和RS-Unet模型的混凝土桥梁裂缝识别

Identification of cracks in concrete bridges through fusing improved ResNet-14 and RS-Unet models

针对噪声影响下的细小混凝土裂缝检测,提出了将改进的深度残差网络(ResNet-14)和基于U形框架的Swin-Unet网络(Revised Swin-Unet, RS-Unet)相融合的混凝土桥梁裂缝检测识别方法 .首先,利用改进的ResNet-14网络对裂缝子块进行识别,去除划痕、剥落等噪声的干扰,并保留裂缝区域;然后,采用RS-Unet网络模型对图像进行像素级分割,完成裂缝特征提取;最后,采用边缘线最短距离法进行宽度计算,并在实验室条件下设计了一套裂缝检测系统用以验证该方法 .试验结果表明:在固定拍摄角度和距离的前提下,融合改进的ResNet-14和RS-Unet网络模型对噪声影响下细小混凝土裂缝的识别效果体现出了良好的抗干扰性和准确性,为其应用于实际工程中提供了重要参考作用.

To address the detection of fine concrete cracks under the influence of noise,this paper pro⁃poses a fusion method that combines the improved deep residual network(ResNet-14)and the Swin-Unet network based on a U-shaped structure(Revised Swin-Unet,RS-Unet)for crack detection and recognition in concrete bridges.Firstly,the improved ResNet network is used to identify crack subblock,eliminating the noise interference such as scratch and spalling,while preserving the fracture area.Then,the RS-Unet network model is utilized for pixel-level segmentation of the images to facilitate the crack feature extraction.Finally,the width of the cracks is calculated using the shortest distance method along the edge lines.To validate the proposed method,a set of crack detection system is designed and tested under laboratory conditions.The experimental results show that under the premise of fixed shoot⁃ing angle and distance,the fusion of the improved ResNet-14 and RS-Unet network model exhibits strong resistance to noise interference and achieves accurate identification of small concrete cracks under the influence of noise,providing valuable insights for its practical application in engineering projects.