Bridge inspection is an important step in preserving and rehabilitating transportation infrastructure for extending their service lives. The advancement of mobile robotic technology allows the rapid collection of a large amount of inspection video data. However, the data are mainly the images of complex scenes, wherein a bridge of various structural elements mix with a cluttered background. Assisting bridge inspectors in extracting structural elements of bridges from the big complex video data, and sorting them out by classes, will prepare inspectors for the element-wise inspection to determine the condition of bridges. This article is motivated to develop an assistive intelligence model for segmenting multiclass bridge elements from the inspection videos captured by an aerial inspection platform. With a small initial training dataset labeled by inspectors, a Mask Region-based Convolutional Neural Network pre-trained on a large public dataset was transferred to the new task of multiclass bridge element segmentation. Besides, the temporal coherence analysis attempts to recover false negatives and identify the weakness that the neural network can learn to improve. Furthermore, a semi-supervised self-training method was developed to engage experienced inspectors in refining the network iteratively. Quantitative and qualitative results from evaluating the developed deep neural network demonstrate that the proposed method can utilize a small amount of time and guidance from experienced inspectors (3.58 h for labeling 66 images) to build the network of excellent performance (91.8% precision, 93.6% recall, and 92.7% f1-score). Importantly, the article illustrates an approach to leveraging the domain knowledge and experiences of bridge professionals into computational intelligence models to efficiently adapt the models to varied bridges in the National Bridge Inventory.

桥梁检查是维护和修复运输基础设施以延长其使用寿命的重要一步。移动机器人技术的进步允许快速收集大量检查视频数据。但是，数据主要是复杂场景的图像，其中各种结构元素的桥梁与杂乱的背景混合在一起。协助桥梁检查员从复杂的大型视频数据中提取桥梁的结构要素，并按类别对其进行分类，将为检查员做好元素逐项检查的准备，以确定桥梁的状况。本文旨在开发一种辅助智能模型，用于从空中检查平台捕获的检查视频中分割多类桥梁元素。使用由检查员标记的少量初始训练数据集，将在大型公共数据集上进行预训练的基于遮罩区域的卷积神经网络转移到多类桥梁元素分割的新任务上。此外，时间相干分析试图恢复假阴性并确定神经网络可以学习改善的弱点。此外，开发了一种半监督式自训练方法，以使经验丰富的检查员参与迭代地完善网络。对开发的深度神经网络进行评估的定量和定性结果表明，该方法可利用少量时间和经验丰富的检查员的指导（用于标记66张图像的时间为3.58小时）来构建性能优良的网络（精度为91.8％，93.6％）召回率和92.7％的F1得分）。重要的，