This research investigates vision‐based automated bridge component recognition, which is critical for automating visual inspection of bridges during initial response after earthquakes. Semantic segmentation algorithms with up to 45 convolutional layers are applied to recognize bridge components from images of complex scenes. One of the challenges in such scenarios is to get the recognition results consistent with high‐level scene structure using limited amount of training data. To impose the high‐level scene consistency, this research combines 10‐class scene classification and 5‐class bridge component classification. Three approaches are investigated to combine scene classification results into bridge component classification: (a) naïve configuration, (b) parallel configuration, and (c) sequential configuration of classifiers. The proposed approaches, sequential configuration in particular, are demonstrated to be effective in recognizing bridge components in complex scenes, showing less than 1% of accuracy loss from the naïve/parallel configuration for bridge images, and less than 1% false positives for the nonbridge images.

中文翻译：

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基于视觉的自动桥梁组件识别，具有高水平的场景一致性

这项研究调查了基于视觉的桥梁自动识别，这对于地震后的初始响应过程中的桥梁视觉自动检查至关重要。具有多达45个卷积层的语义分割算法被应用于从复杂场景的图像中识别桥成分。在这种情况下的挑战之一是使用数量有限的训练数据来获得与高级场景结构一致的识别结果。为了实现较高的场景一致性，本研究将10类场景分类和5类桥梁组件分类相结合。研究了三种将场景分类结果组合到桥构件分类中的方法：（a）天真配置，（b）并行配置和（c）分类器的顺序配置。