Semantic segmentation of closed‐circuit television (CCTV) images can facilitate automatic severity assessment of sewer pipe defects by assigning defect labels to each pixel on the image, from which defect types, locations, and geometric information can be obtained. In this study, a unified neural network, namely DilaSeg‐CRF, is proposed by fully integrating a deep convolutional neural network (CNN) with dense conditional random field (CRF) for improving the segmentation accuracy. First, DilaSeg is constructed with dilated convolution and multiscale techniques for producing feature maps with high resolution. The steps of the dense CRF inference algorithm are converted into CNN operations, which are then formulated as recurrent neural network (RNN) layers. The DilaSeg‐CRF is proposed by integrating DilaSeg with the RNN layers. Images containing three common types of sewer defects are collected from CCTV inspection videos and are annotated with ground truth labels, after which the proposed models are trained and evaluated. Experiments demonstrate that the end‐to‐end trainable DilaSeg‐CRF can improve the segmentation significantly, with an increase of 32% and 20% in mean intersection over union (mIoU) values compared with fully convolutional network (FCN‐8s) and DilaSeg, respectively. Our proposed DilaSeg‐CRF also achieves faster inference speed than FCN and eliminates the manual postprocessing for refining the segmentation results.

中文翻译：

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结合条件随机场的统一卷积神经网络用于管道缺陷分割

通过为图像上的每个像素分配缺陷标签，闭路电视（CCTV）图像的语义分割可以帮助自动进行下水道缺陷的严重性评估，从中可以获得缺陷的类型，位置和几何信息。在这项研究中，通过将深度卷积神经网络（CNN）与密集条件随机场（CRF）完全集成，提出了一个统一的神经网络DilaSeg-CRF，以提高分割精度。首先，用膨胀卷积和多尺度技术构造DilaSeg，以产生高分辨率的特征图。密集CRF推理算法的步骤被转换为CNN运算，然后将其表示为递归神经网络（RNN）层。DilaSeg-CRF是通过将DilaSeg与RNN层集成而提出的。从CCTV检查视频中收集包含三种常见类型的下水道缺陷的图像，并用地面真相标签进行注释，然后对提出的模型进行训练和评估。实验表明，端到端可训练的DilaSeg-CRF可以显着改善分割效果，与完全卷积网络（FCN-8s）和DilaSeg相比，平均相交度（mIoU）值分别提高了32％和20％，分别。我们提出的DilaSeg-CRF的推理速度也比FCN快，并且省去了手动后处理以完善分割结果的能力。实验表明，端到端可训练的DilaSeg-CRF可以显着改善分割效果，与完全卷积网络（FCN-8s）和DilaSeg相比，平均相交度（mIoU）值分别提高了32％和20％，分别。我们提出的DilaSeg-CRF的推理速度也比FCN快，并且省去了手动后处理以完善分割结果的能力。实验表明，端到端可训练的DilaSeg-CRF可以显着改善分割效果，与完全卷积网络（FCN-8s）和DilaSeg相比，平均相交度（mIoU）值分别提高了32％和20％，分别。我们提出的DilaSeg-CRF的推理速度也比FCN快，并且省去了手动后处理以完善分割结果的能力。