为解决传统钢结构表面锈蚀检测中缺乏检测标准且锈蚀难以量化的难题，本文基于深度神经网络提出了一种新型锈蚀检测方法，通过对锈蚀图像进行语义分割来实现锈蚀区域的检测与定量分析。设计深度神经网络时采用“编码器-解码器”架构，将网络计算流程分为编码阶段（降采样）与解码阶段（上采样）两部分，最终得到与输入图像长宽一致的分割模板，用以表示每个像素点是否为锈蚀。采用了苏通大桥锈蚀数据集（包括锈蚀图像440张，图像分辨率为709×1067）训练网络并对其进行了数据增强，最终得到6156张经人工标注过的彩色图像，图像分辨率为256×256。网络训练经过50次循环总耗时约7 h，最终正确率可以达到训练集92.55%和验证集90.56%。此外还在原始锈蚀图像上进行检测，结果显示分割网络可以识别出图像中锈蚀区域的主体部分。为进行锈蚀的定量分析定义了“锈蚀面积”、“锈蚀率”、“总体锈蚀率”等评价指标，通过分割网络可以直接计算得到这些锈蚀区域的定量指标，为钢结构日常管理养护提供数据支撑。

A novel detection method based on deep neural networks is proposed in this article to handle the tough problem of standardizing and quantifying in corrosion detection, and detection as well as quantitative analysis is achieved through semantic segmentation on corrosion images. The “encoder-decoder” architecture is selected in designing deep neural networks, which divides the process of computation into encoding part (downsampling) and decoding part (upsampling), and a segmentation mask with the same resolution as the input image is obtained after computation which indicates whether a pixel belongs to corrosion. Sutong bridge corrosion dataset including 440 corrosion images with resolution of 709×1067 is used to train the segmentation network, and raw images are augmented to 6156 manual labeled images with resolution of 256×256. The training process took about 7 h containing 50 epochs with binary accuracy of 92.55% in training set and 90.56% in validation set. Besides, segmentation network is also applied on raw images and the detection results show that majority of corrosion can be recognized. Corrosion area, corrosion rate and total corrosion rate are defined to quantitatively analyze the corrosion regions, and these indices can be directly calculated with segmentation mask which provide data support for daily maintenance for steel structures.