The detailed monitoring of jointed plain concrete pavement (JPCP) slab condition is essential for cost-effective JPCP maintenance and rehabilitation. However, existing visual inspection practices for detailed slab condition classification are time-consuming and labor-intensive. In this paper, we proposed an automated JPCP slab condition classification model based on convolutional neural networks (ConvNets), which is the first to perform multi-label classification on the JPCP slab condition based on both crack types and severity levels. To handle the different scales between JPCP slab condition states, the model includes a novel global context block with atrous spatial pyramid pooling, denoted as a GC-ASPP block. The block can be flexibly applied to any ConvNets to effectively model the global context of images with the extraction of multiscale image features. The proposed model was evaluated using real-world 3D JPCP surface data. With the GC-ASPP block, our best model achieved an average precision of 85.42% on multi-label slab condition classification.

中文翻译：

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用于连接素混凝土路面条件自动分类的卷积神经网络

对连接素混凝土路面 (JPCP) 板条件的详细监测对于具有成本效益的 JPCP 维护和修复至关重要。然而，现有的用于详细板坯状态分类的目视检查实践既费时又费力。在本文中，我们提出了一种基于卷积神经网络 (ConvNets) 的自动 JPCP 板条条件分类模型，该模型是第一个基于裂纹类型和严重程度对 JPCP 板条条件进行多标签分类的模型。为了处理 JPCP 板条件状态之间的不同尺度，该模型包括一个具有多孔空间金字塔池化的新型全局上下文块，表示为 GC-ASPP 块。该块可以灵活地应用于任何 ConvNets，通过提取多尺度图像特征来有效地对图像的全局上下文进行建模。所提出的模型是使用真实世界的 3D JPCP 表面数据进行评估的。使用 GC-ASPP 块，我们的最佳模型在多标签板条件分类上实现了 85.42% 的平均精度。