Image segmentation has been implemented for pavement defect detection, from which types, locations, and geometric information can be obtained. In this study, an integration of a fully convolutional network with a Gaussian‐conditional random field (G‐CRF), an uncertainty framework, and probability‐based rejection is proposed for detecting pavement defects. First, a fully convolutional network is designed to generate preliminary segmentation results, and a G‐CRF is used to refine the segmentation. Second, epistemic and aleatory uncertainties in the model and database are considered to overcome the disadvantages of traditional deep‐learning methods. Last, probability‐based rejection is conducted to remove unreasonable segmentations. The proposed method is evaluated on a data set of images that were obtained from 16 highways. The proposed integration segments pavement distresses from digital images with desirable performance. It also provides a satisfactory means to improve the accuracy and generalization performance of pavement defect detection without introducing a delay into the segmentation process.

中文翻译：

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具有全卷积网络和不确定性框架的路面缺陷检测

图像分割已用于路面缺陷检测，可从中获取类型，位置和几何信息。在这项研究中，提出了将全卷积网络与高斯条件随机场（G-CRF），不确定性框架和基于概率的剔除进行集成以检测路面缺陷的方法。首先，设计一个全卷积网络以生成初步的分割结果，然后使用G-CRF细化分割。其次，考虑了模型和数据库中的认识和不确定的不确定性，以克服传统深度学习方法的缺点。最后，进行基于概率的拒绝以去除不合理的细分。在从16条高速公路获得的图像数据集上评估提出的方法。所提出的集成将具有良好性能的数字图像中的路面困扰分割开。它还提供了令人满意的手段来提高路面缺陷检测的准确性和综合性能，而不会在分割过程中引入延迟。