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Thanks to the development of deep learning, the use of data-driven methods to detect pavement distresses has become an active research field. This research makes four contributions to address the problem of efficiently detecting cracks and sealed cracks in asphalt pavements. First, a dataset of pavement cracks and sealed cracks is created, which consists of 10, 400 images obtained by a vehicle equipped with a highway condition monitor, with 202, 840 labeled distress instances included in these pavement images. Second, we develop a dense and redundant crack annotation method based on the characteristics of the crack images. Compared with traditional annotation, the method we propose generates more object instances, and the localization is more accurate. Next, to achieve efficient crack detection, a semi-automatic crack annotation method is proposed, which reduces the working time by 80% compared with fully manual annotation. Finally, comparative experiments are conducted on our dataset using 13 currently prevailing object detection algorithms. The results show that dense and redundant annotation is effective; moreover, cracks and sealed cracks can be efficiently and accurately detected using the YOLOv5 series model and YOLOv5s is the most balanced model with an F1-score of 86.79% and an inference time of 14.8ms. The pavement crack and sealed crack dataset created in this study is publicly available.
As urbanization increases, highway networks have been developed to meet challenges and demands in transportation. As highways comprise part of the critical infrastructure for public transportation, promoting advanced analysis and assessment technology for highway systems is an important part of an intelligent transportation system (ITS). In the past decade, an increasing number of highways have been damaged due to the poor environment, car overload, material aging, and so on [1]. Pavement distress detection, identification and classification are important steps in a pavement management system (PMS). It helps the agency to determine the appropriate rehabilitation techniques to be performed on the pavement [2]. Generally, cracks are the earliest signs of pavement distress. The continuous propagation of cracks without proper treatment in the early stages of damage will result in high maintenance costs and severe consequences. Therefore, detecting cracks and repairing them quickly are essential tasks for highway maintenance departments. Normally, highway maintenance workers use sealants to repair cracks. However, when the pavement structure has been damaged, cracks are often more likely to develop around the sealed crack, so it is also necessary to detect sealed cracks. The traditional routine of highway pavement distress inspection relies on manual on-site surveys, which are labor-intensive and time-consuming. In addition, highways are dangerous working environments for road inspection personnel [3]. It is therefore necessary to develop automatic and efficient methods of detecting highway pavement cracks and sealed cracks.
At present, special road condition inspection devices have been extensively studied. Non-contact devices such as thermal imaging sensors [4] and ground-penetrating radar [5] have been utilized to detect cracks and take advantage of the differences in the signals returned from normal and damaged pavements. Embedded fiberoptic sensors [6] are also an emerging technology used to detect pavement distress. Although the tools mentioned above can accurately locate pavement distress, their high cost and low efficiency are critical drawbacks in real-world scenarios, preventing them from being applied to a wider range of situations. Image acquisition methods based on charge-coupled devices (CCDs) and complementary metal oxide semiconductor (CMOS) sensors are prevalent because of their efficiency and low cost. However, the detection of pavement distress using image processing is still a very challenging task, especially for asphalt pavements. The reasons for this can be summarized as follows:
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Image processing methods such as transformation, enhancement, and segmentation have gained the attention of researchers in the field of pavement assessment, but these methods are empirical [3], i.e., they require constant adjustment of the parameters to achieve an optimum result. Traditional machine learning methods, such as random forest [8, 9] and AdaBoost [10], have also been used to detect cracks in pavements. The problem is that such methods can only obtain low-level image information and cannot extract high-level semantic information, which has a significant impact on the robustness of the algorithms. With the increase of parallel computing power and the development of deep learning, data-driven methods are widely used in PMS in various countries [11, 12]. Convolutional neural network (CNN) [13] is one of the well-known data-driven methods that can automatically learn high-level information from large amounts of data through a multilayered artificial neural network (ANN), which can be used to classify images or detect objects.
In a real-world scenario, the low practicality and inefficiency of pavement inspection systems are difficult problems for road maintenance departments. Research institutions and companies in many countries have developed automated road condition monitoring vehicles that automatically detect road damage at normal traffic flow speeds while a large number of road images are collected using CCD or CMOS sensors mounted on the rear of the vehicle. The use of an efficient and practical system to process this enormous amount of data and detect cracks and sealed cracks within them was the focus of this study. The contributions of this study are as follows:
The paper’s structure comprises five sections, complemented by the current Introduction. Section 2 presents related work in the field of crack detection in recent years. Details of our newly developed dataset are presented in Section 3. Section 4 describes the specific experimental settings. Section 5 illustrates the results, including a detailed comparison of different models and a discussion. Section 6 concludes the study.
Pdf] Assessing Approaches To Appraisal
In this section, we present various studies that were proposed by different researchers for pavement distress detection. As shown in Table 1, we focus on the pavement distress dataset, image annotation type, and detection model.
Having sufficient data is the cornerstone of using deep learning to detect pavement distress. There have been several attempts by researchers to build pavement image datasets through a variety of image acquisition methods. The pavement damage dataset built by Maeda et al. [17] using a smartphone contains 9053 images with eight different damage types. Here, the smartphone was mounted on top of a vehicle dashboard to capture the road ahead, so the quality of the images was affected by vehicle vibration; moreover, since the images were captured approximately 10 m ahead, only the easily distinguishable road damage, such as potholes, coarse cracks, and blurred white lines, can be found in the images. The German asphalt pavement distress (GAPs) dataset [14] was acquired using a high-resolution camera suspended behind a high-speed measurement vehicle and contains 2468 grayscale images with six damage types, and the original images were segmented into sub-images with a resolution of 64 × 64 pixels for classification. The data in the GAPs dataset come from three different federal roads in Germany, with the data from two of the roads serving as the training and validation sets, and the data from the other road serving as the test set only. The GAPs classification results show that when there is a difference between the training and the test set, the performance of the ANN on the test set deteriorates significantly, even if the ANN has achieved good results on the training set. The dataset created by Maniat et al. [15] is from Google Street View and the main drawbacks are the presence of many artifacts and the low quality of the images, but using Google Street View is a very economical way to obtain pavement data. Wu et al. [21] used a camera to capture 95, 000 images for segmenting cracks, and the method they used is based on a data-driven full convolutional network (FCN), which means that in the training set, each object needs to be accurately labeled manually using polygons, which can be labor- and resource-intensive. It is also a challenge to apply this rapidly in practice, and the data are not publicly available.
Many researchers do not have enough data. Amhaz et al. [26], Oliveira et al. [27], and Shi et al. [8] used a segmentation-based method to detect
Pi(18:1/18:1) Is A Scd1 Derived Lipokine That Limits Stress Signaling
In this section, we present various studies that were proposed by different researchers for pavement distress detection. As shown in Table 1, we focus on the pavement distress dataset, image annotation type, and detection model.
Having sufficient data is the cornerstone of using deep learning to detect pavement distress. There have been several attempts by researchers to build pavement image datasets through a variety of image acquisition methods. The pavement damage dataset built by Maeda et al. [17] using a smartphone contains 9053 images with eight different damage types. Here, the smartphone was mounted on top of a vehicle dashboard to capture the road ahead, so the quality of the images was affected by vehicle vibration; moreover, since the images were captured approximately 10 m ahead, only the easily distinguishable road damage, such as potholes, coarse cracks, and blurred white lines, can be found in the images. The German asphalt pavement distress (GAPs) dataset [14] was acquired using a high-resolution camera suspended behind a high-speed measurement vehicle and contains 2468 grayscale images with six damage types, and the original images were segmented into sub-images with a resolution of 64 × 64 pixels for classification. The data in the GAPs dataset come from three different federal roads in Germany, with the data from two of the roads serving as the training and validation sets, and the data from the other road serving as the test set only. The GAPs classification results show that when there is a difference between the training and the test set, the performance of the ANN on the test set deteriorates significantly, even if the ANN has achieved good results on the training set. The dataset created by Maniat et al. [15] is from Google Street View and the main drawbacks are the presence of many artifacts and the low quality of the images, but using Google Street View is a very economical way to obtain pavement data. Wu et al. [21] used a camera to capture 95, 000 images for segmenting cracks, and the method they used is based on a data-driven full convolutional network (FCN), which means that in the training set, each object needs to be accurately labeled manually using polygons, which can be labor- and resource-intensive. It is also a challenge to apply this rapidly in practice, and the data are not publicly available.
Many researchers do not have enough data. Amhaz et al. [26], Oliveira et al. [27], and Shi et al. [8] used a segmentation-based method to detect
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