Automated pixel-level crack detection is one of the essential tasks in the field of defect inspection. Deep convolutional neural networks, typically using encoder–decoder architectures, have been successfully applied to many crack detection scenes in recent works. However, encoder–decoder networks commonly rely on downsampling and upsampling operations and have a large number of parameters, which may influence the accuracy of crack prediction due to the cracks usually have long, narrow sizes, and the labeled training set is always limited. To address these issues, we propose a simple and effective hybrid atrous convolutional network (HACNet). HACNet maintains the same spatial resolution throughout the whole architecture. It can retain more spatial precision in prediction. HACNet uses atrous convolutions with the proper dilation rates to enlarge the receptive field and a hybrid approach connecting these convolutions to aggregate multiscale features. The resulting architecture can achieve accurate segmentation with relatively few parameters. Evaluations on the public CFD data set, CrackTree206 data set, Deepcrack data set (DCD), and Yang et al. Crack data set (YCD) demonstrate that our method can obtain promising results, compared with other recent approaches. Evaluation on self-collected images and SDNET2018 data set illustrates the good potential of HACNet for practical applications.

中文翻译：

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用于像素级裂纹检测的有效混合Atrous卷积网络

自动化的像素级裂缝检测是缺陷检查领域的基本任务之一。深度卷积神经网络通常使用编码器-解码器体系结构，在最近的工作中已成功应用于许多裂缝检测场景。但是，编码器-解码器网络通常依赖于下采样和上采样操作，并且具有大量参数，这可能会影响裂缝预测的准确性，因为裂缝通常长而窄，而且标注的训练集始终受到限制。为了解决这些问题，我们提出了一种简单有效的混合无规卷积网络（HACNet）。HACNet在整个架构中保持相同的空间分辨率。它可以在预测中保留更多的空间精度。HACNet使用具有适当扩张率的无规则卷积来扩大接收场，并使用一种混合方法将这些卷积连接到聚合的多尺度特征。生成的体系结构可以使用相对较少的参数实现准确的分段。对公共CFD数据集，CrackTree206数据集，Deepcrack数据集（DCD）和Yang的评估等。裂纹数据集（YCD）证明，与其他最新方法相比，我们的方法可以获得令人满意的结果。对自我收集的图像和SDNET2018数据集的评估说明了HACNet在实际应用中的巨大潜力。