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With the development of state-of-the-art algorithms, pavement distress can already be detected automatically. However, most pavement distress detection is currently implemented as a single task, either at the region level or at the pixel level. To comprehensively assess the pavement condition, a multitask fusion model, Pavement Distress Detection Network (PDDNet), was proposed for integrated pavement distress detection at both the region level and pixel level. PDDNet was trained and tested on distress images captured via unmanned aerial vehicle (UAV), and seven types of pavement distresses were investigated and analyzed. Compared with Mask Region-based Convolutional Neural Network (R-CNN), U-Net, and W-segnet, PDDNet shows higher performance in classification, localization, and segmentation of pavement distresses. Results demonstrate that PDDNet achieves region-level and pixel-level detection of seven types of distresses with the mean average precision of 0.810 and 0.795, respectively. As a portable and lightweight device, the UAV can collect full-width pavement distress images, which helps improve the efficiency of pavement distress detection. [ABSTRACT FROM AUTHOR]

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Timely detection of early pavement distress is crucial for effective pavement maintenance. A promising approach involves the segmenting the road surface cracks area from its images. However, the previously employed full-resolution residual network (FRRN), when employing a large number of full-resolution residual units (FRRUs), is susceptible to challenges such as exploding and vanishing gradients. Addressing these issues, this paper has introduced a depth supervision mechanism into FRRN, leading to the development of the Depth Supervision FRRN (DSFRRN) model, designed specifically for accurate segmentation of cracks in road images. This model enhances nonlinear expressiveness while retaining essential features, achieving high precision in pavement crack segmentation. Through experimental validation using a dataset comprising 1565 images of damaged pavements, the result demonstrate that DSFRRN outperforms the original FRRN in terms of both F1 score and Jaccard index, accurately delineating cracks. Consequently, the automated pavement damage detection method presented herein facilitates cost-effective and efficient pavement inspection, contributing to the safeguarding of roadway surfaces and transportation infrastructure. [ABSTRACT FROM AUTHOR]

Pavement distress detection is of significance for road maintenance and traffic safety. Manual pavement distress detection suffers from high workloads, inefficiency, low accuracy, and high cost. To replace the manual operations in the pre-filling detection with the aim to improve efficiency and reduce cost, this paper proposes a three-stage automatic inspection and evaluation system for pavement distress based on improved deep convolutional neural networks (CNNs). First, the system integrates multi-level context information from the CNN classification model to construct discriminative super-features to determine whether there is distress in the pavement image and the type of the distress, so as to achieve rapid detection of pavement distress. Then, the pavement images with distress are fed into the CNN segmentation model to highlight the distress region with pixel-wise. In the segmentation model, a novel pyramid feature extraction module and a novel guidance attention mechanism are introduced. Finally, we evaluate the degree of pavement damage according to the segmentation results of the CNN segmentation model. In the experiments, we compare our classification model and segmentation model with other state-of-the-art methods on two pavement distress datasets, and the results demonstrate that the proposed models achieve out-performance on different evaluation metrics. [ABSTRACT FROM AUTHOR]

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on https://github.com/DearCaat/ioplin , and the CQU-BPDD dataset is able to be accessed on https://dearcaat.github.io/CQU-BPDD/. [ABSTRACT FROM AUTHOR]