Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-theart methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

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Comfort is a major requirement in planning pedestrian facilities. Pedestrians walk where they feel comfortable and when they do not feel at ease, they walk elsewhere. A typical example is that filthy, distressed, or too narrow sidewalks induce pedestrians to walk on carriageways. This behaviour jeopardizes road safety and highly dangerous to most users, leave them vulnerable. Unsuitable pavements can be the result of irregular maintenance\ud operations to restore evenness after shock damage, weather phenomena, installation of equipment (e.g., posts, fences, urban furniture) with a reduction of walkable surface, or substandard repair work on pavements and patches due to emergency operations. These problems can be solved with an appropriate maintenance management system, which optimizes financial resources to make smart decisions about how to intervene with an adequate and lasting maintenance operation. This paper defines an evaluation index for sidewalk conditions as a part of an efficient set-up of a Sidewalk Management System, which is similar to the better known Road Management System. The study relies on surveys, as well as the classification and analysis of sidewalk distresses. The authors adapted an index already standardized by ASTM for roads and airports: the Pavement Condition Index (PCI). PCI has been modified to consider the specific types on the sidewalks studied within this paper. To validate the method, a case study of a residential district in Rome, Italy, was carried out. The chosen area lacks regular maintenance and has therefore resulted in a network of unsafe sidewalks. Frequent detour routes were surveyed and related to the level of distresses within a general assessment of safety. This study concentrates on sidewalks with flexible pavements cause this type of pavement is the only one adopted in the survey areas and, in general, throughout Italy