人工检测是在役桥梁管理与维护中主要的工作任务之一，通过人工目测或工具量测形成的各类病害检测结果是指导桥梁运维决策的关键基础数据。然而，因缺少对人工检测病害数据的标准化快速批处理方法，该类数据往往未能得到充分挖掘和利用；现有方法呈现出数据处理难度大、成本高、时效性弱等问题，难以支撑实桥管养和相关科研需要。面对人工检测采集的海量原始病害数据，本研究以铺装病害人工检测数据为例，提出一种基于图像处理的人工检测表观病害数据标准化批处理方法。首先通过人工检测采集标注的CAD电子化数据，进行病害图像网格式栅格化批处理，形成以吊杆（或斜拉索）的锚固点网格化划分的病害分布位图；其次通过图像连通域查找和判定算法，提取病害的面积、长度等信息；最后，根据网格标签输出可供计算机快速分析处理的标准化数据，并进行病害数据分析，包括病害类型分析、位置分析、横向分布分析和纵向分布分析等。该方法实现了快速化和标准化的人工检测病害大数据处理整理，为相关研究和针对性指导桥梁养护提供了行之有效的基础数据处理方法；此外，所提出的标准化批处理方法对于人工检测中的量大面广的其他构件病害数据及其他类型数据处理分析具有较强的可移植性和重要参考意义。

Manual inspection is one of the main tasks in the management and maintenance of bridges. The inspection results of various damages provided by manual visual inspection or measurement is the key basic data for the decision-making of bridge operation and maintenance. However, due to the lack of standardized batch-processing method for manual inspection data, this kind of data is not fully utilized in the past practice. With the traditional methods, the process of those data is laborious, high cost and low timeliness. Therefore, these methods can not meet the requirements of bridge management and research. In consideration of those massive original damage data collected by manual inspection, this study proposed a standardized manual-marked pavement damage data-processing method based on image processing. Firstly, the labeled electronic CAD data was collected by manual inspection, and the damage formed image rasterization batch-processing was carried out to produce the damage distribution bitmap divided by the anchor points of suspenders (or stay cables). Secondly, the area, length and other information of the damage were extracted through the searching and assessment of the image connected components. Finally, the standardized data was processed, analyzed and output by the computer according to the grid labels in no time, and the damage data analysis was carried out, which including damage type analysis, location analysis, horizontal distribution analysis and vertical distribution analysis. The proposed method realizes the rapid and standardized big data processing and collation of manual inspection damage data, and provides an effective basic data processing method for the related researches and targeted guidance of bridge maintenance. In addition, the proposed standardized batch-processing method is quite portable and can provide reference for the processing and analysis of large amount and wide range of other component damage data and other types of data in manual inspection.