Compared with the artificial crack detection method, the bridge crack recognition method based on computer vision has the advantages of high efficiency, easy operation and low cost. However, under the condition of moving (UAV) shooting, the crack images collected often have quality defects such as low definition, complex background, severe interference by light and noise. especially when faced with small cracks in early development, some traditional crack detection algorithms with high requirements on crack images cannot be well adapted. In this paper, an automatic recognition technology for surface cracks of bridges is proposed, which is suitable for mobile machine vision detection. The core of the technology is to obtain high-precision two-dimensional spectrum estimation of crack images by using two-dimensional amplitude and phase estimation method (ab. 2D-APES), and then to enhance the crack information by filtering low-frequency information, so as to realize the automatic recognition of crack targets in images. An industrial-grade drone (DJI Jingwei M200V2) equipped with a high-definition zoom image acquisition system was used to acquire images of the bottom and sides of the bridge of the Minpu Bridge in Shanghai. After locating, magnifying and cutting the apparent crack image of concrete, and then using the above method, the crack automatic identification was realized. Results show that the high-precision non-parametric amplitude spectrum analysis method can adapt to the situation of poor image quality of the UAV, and thus provides a feasible solution for the automatic identification of concrete cracks based on mobile machine vision.

与人工裂缝检测方法相比，基于计算机视觉的桥梁裂缝识别方法具有效率高，操作简便，成本低的优点。然而，在移动（UAV）拍摄的条件下，收集的裂纹图像经常具有质量缺陷，例如低清晰度，背景复杂，受到光和噪声的严重干扰。特别是在早期开发中遇到小裂缝时，对裂缝图像要求很高的一些传统裂缝检测算法无法很好地适应。本文提出了一种桥梁表面裂纹的自动识别技术，该技术适用于移动机器视觉检测。该技术的核心是通过使用二维幅度和相位估计方法（ab。2D-APES）获得裂纹图像的高精度二维光谱估计，然后通过过滤低频信息来增强裂纹信息。 ，从而实现图像中裂纹目标的自动识别。配备高清变焦图像采集系统的工业级无人机（大疆经纬M200V2）用于采集上海民浦大桥桥底和侧面的图像。在定位，放大和切割混凝土的表观裂缝图像后，再采用上述方法，实现了裂缝的自动识别。结果表明，高精度非参数振幅谱分析方法可以适应无人机图像质量较差的情况，