Computer vision algorithms are powerful techniques that can be used for remotely monitoring and inspecting civil structures. Detecting and segmenting cracks in images of concrete bridges can provide useful information related to the health of the structure. There are several states of the art methods based on Deep Learning that have been used for segmentation tasks. However, most of them require a large number of parameters that limits their use in mobile device applications. Here, we propose a DenseNet architecture with only 13 layers with one feature extractor stage and two datapaths. Implementations of state of the art semantic segmentation models are also tested. The proposed model achieves better results than standard algorithms with only a fraction of the parameters making it suitable for developing mobile device applications for bridge structure monitoring. As an additional contribution, two new databases for semantic segmentation of cracks are presented. These databases are used to test all the algorithms in this work and will be available upon request. Additional experiments using a public database are also performed for the sake of comparison. The best results are obtained using the proposed DenseNet-13 architecture with only 350,000 parameters achieving an Intersection Over Union of 94.51% for crack semantic segmentation.

计算机视觉算法是可用于远程监视和检查土木结构的强大技术。检测和分割混凝土桥梁图像中的裂缝可以提供有关结构健康的有用信息。有几种基于深度学习的先进方法已用于细分任务。但是，它们大多数都需要大量参数，这限制了它们在移动设备应用程序中的使用。在这里，我们提出了仅具有13个层的DenseNet体系结构，其中包含一个特征提取器阶段和两个数据路径。还测试了最新语义分割模型的实现。所提出的模型仅使用参数的一小部分即可获得比标准算法更好的结果，使其适合开发用于桥梁结构监控的移动设备应用程序。作为一个额外的贡献，提出了两个用于裂纹语义分割的新数据库。这些数据库用于测试这项工作中的所有算法，并可根据要求提供。为了比较，还使用公共数据库进行了其他实验。使用拟议的DenseNet-13架构仅使用350,000个参数即可获得最佳结果，从而实现94.51％的联合语义交集用于裂纹语义分割。这些数据库用于测试这项工作中的所有算法，并可根据要求提供。为了比较，还使用公共数据库进行了其他实验。使用拟议的DenseNet-13架构仅使用350,000个参数即可获得最佳结果，从而实现94.51％的联合语义交集用于裂纹语义分割。这些数据库用于测试这项工作中的所有算法，并可根据要求提供。为了比较，还使用公共数据库进行了其他实验。使用拟议的DenseNet-13架构仅使用350,000个参数即可获得最佳结果，从而实现94.51％的联合语义交集用于裂纹语义分割。