针对传统桥梁裂缝检测算法不能准确提取裂缝的问题,提出了一种复杂背景下基于图像处理的桥梁裂缝检测算法。根据深度卷积生成式对抗网络原理,利用桥梁裂缝图像生成模型,对数据集进行扩增。针对裂缝特征构建基于语义分割的桥梁裂缝图像分割模型,利用桥梁裂缝图像分割模型提取高分辨率裂缝图像中的裂缝。研究结果表明,与现有算法相比,所提算法在复杂道路场景中具有更好的检测效果和更强的泛化能力。

In order to solve the problem that the traditional bridge crack detection algorithm cannot extract cracks accurately, a bridge crack detection algorithm is proposed based on image processing, which is suitable for complex scenes. According to the principle of the deep convolutional generative adversarial network, the bridge crack image generative model is proposed and used to amplify the dataset. For the characteristics of bridge cracks, a bridge crack image segmentation model is constructed based on semantic segmentation. The bridge crack image segmentation model is used to extract the bridge cracks from the high-resolution crack images. The research results show that the proposed algorithm has a better detection effect and a stronger generalization ability in the complex road scenes compared with the existing algorithms.