采用U-Net卷积网络的桥梁裂缝检测方法

针对传统的桥梁裂缝检测准确性不高、丢失细节信息、宽度信息获取困难等问题,提出一种采用U-Net卷积网络的像素级、小样本的裂缝检测方法。该方法使用多层卷积自动提取裂缝特征,并利用浅层网络与深层网络叠加的方法实现裂缝局部特征与抽象特征的融合,从而保留裂缝细节特征,使得检测准确性大大提升。对检测结果中出现的背景杂波和伪裂缝,采用阈值法和改进的迪杰斯特拉连接算法来实现裂缝的精细提取。最后,采用八方向搜索法实现裂缝宽度的精确测量。实验证明,所提方法能准确、完整地对桥梁裂缝进行提取,宽度测量准确,可以满足应用需求。

Method for bridge crack detection based on the U-Net convolutional networks

In order to improve the accuracy of bridge crack detection, retain details, and get information on the crack width, the paper proposes a pixel-wise and small sample crack detection method by using U-Net convolutional neural networks. The method uses a U-Net network to extract crack features automatically by using multi-layer convolutions, and uses the superposition of the shallow network and deep network to realize the fusion of local features and abstract features of cracks. This method can retain the details of cracks and greatly improve the accuracy of detection. In order to refine the detection results, the paper presents the threshold method and an improved Dijkstra minimum spanning tree algorithm for eliminating noise and pseudo cracks. Finally, an eight-direction searching method is applied to measure the width of cracks in pixels. Experiments prove that the proposed method can accurately and completely detect bridge cracks and measure the width, which can meet the application requirements.