Recently, many deep learning methods have achieved great results in the field of automated pavement distress detection, but most of them ignore other types of distresses beyond cracks. This paper proposes an efficient deep learning framework for automated asphalt pavement distress segmentation called pavement distress segmentation network (PDSNet). PDSNet can effectively segment multiple asphalt pavement distresses, including crack, pothole, raveling, patch, and sealed crack. It consists of two parallel feature extraction branches. One is the P branch to extract prior global information. The other is the U branch to obtain local information. By utilising the global and local features together, PDSNet can produce precise segmentation results under complicated circumstances. For deep learning purposes, a pavement distress dataset consisting of 4000 pavement images is collected and manually labelled at pixel level. Each image of the pavement dataset is a two-channel image, which is concatenated by a 2D pavement image and the correspondingly 3D pavement image. Particularly, it is the first pavement distress dataset that utilises two-channel pavement images. According to the experimental results, PDSNet yields a performance with a MIoU of 83.7%. Compared with the state-of-the-art networks, PDSNet achieves the best MIoU and has considerable parameter number and inference time.

Automatic detection and segmentation of potholes and raveling are crucial for preventive maintenance and ensuring roads structural health. However, the extraction of pavement potholes was mainly based on traditional image processing methods, which have proven to be ineffective and inaccurate. Additionally, the absence of a unified pothole and raveling dataset has resulted in the lack of benchmarks for evaluating various methods. This work makes three contributions to address these problems. Firstly, we have curated the pavement pothole and raveling distress detection and segmentation datasets. Secondly, a two-step pavement pothole and raveling detection and segmentation method was proposed. In the initial step, an automated pavement pothole and raveling detection model was developed using the modified YOLOX. Subsequently, the segmentation model, named dual self-attention segmentation network (DSASNet), was proposed to segment distress by extracting mode-sensitive features from intensity and range images using two parallel Twins-SVT self-attention branches. Moreover, we design a mid-fusion module to adaptively fuse mode-specific and scale-specific features. Finally, a pyramid pooling module (PPM) is connected to further enhance the segmentation capability for potholes and raveling of various sizes and shapes. The F1-score and Intersection over union (IoU) of the proposed DSASNet on the test set are 93.65% and 0.881, respectively, outperforming other baseline methods. Furthermore, we conduct an experiment to quantitatively compare the two-step method with the one-step method using only a single semantic segmentation model. The results demonstrated clear advantages of the proposed two-step method in terms of accuracy and efficiency for pavement pothole and raveling segmentation.

The use of three-dimensional (3D) pavement distress detection mechanisms has become a trend in pavement surface condition evaluation, including crack classifications and ratings. Inaccurate crack classification could affect indicators of crack detection, as represented by the area of each cracking region. Therefore, development of intelligent crack detection models are necessary and important. This paper focuses on irregular cracks, such as linear and netted types. The primary purpose of the research summarized in the paper is to develop a multiscale clustering model by taking advantage of 3D pavement surface images. A preprocessing procedure is carried out to obtain an initially identified crack image (by a CrackSeed-based approach). A cracking region is then characterized by a minimum circumscribed rectangle as the basic unit of the clustering. Then multiscale features are used as the crack unit. Linear crack categories and intensities are classified according to an orientation angle to the length-width ratio, while netted cracks are recognized on the basis of block features. Meanwhile, the optimized spatial distance between the crack subblocks is calculated to precisely locate pavement crack. Finally, a multiscale criterion is applied to quantitatively analyze the degree of road surface damage. In classifying transverse cracks, longitudinal cracks, block cracks, and alligator cracks in 320 crack images, the classification precision is 92.8%, 95.2%, 87.8%, and 97.6%, respectively. Experimental results indicate that the proposed method has good precision for 3D pavement crack detection and classification and could provide reliable suggestions for pavement protective maintenance operations to extend service life.

Roads are the foundation of intelligent transportation systems, yet cracks are widely present in roads and seriously affect system performance. Cracks not repaired promptly can develop into severe road defects, significantly increasing the risk of traffic accidents. Researchers in the community have started to focus on the automatic sensing of cracks in asphalt pavements, while it is still a challenging task on concrete pavements. Cracks in the concrete pavement are easily recognized as interrupted segments rather than a continuous whole due to the interference of the surface texture. This mistake can seriously mislead the judgment of cracks and subsequent road repair. In this paper, we aim to solve the challenge by enhancing contextual information about cracks within the images. We first extract the information from the local and global representations using image information and then fuse it into complete contextual information by a designed multilayer perceptron. Finally, we use the discriminative loss to constrain the edges of cracks and backgrounds using complete crack contextual information. We have collected and annotated several images of concrete pavements from several significant provinces in China. Experiments show that our method achieves the best performance compared to state-of-the-art methods, especially in edge determination.

The safety situation of hazardous materials (hazmat) accidents during road transportation in China is severe and very serious accidents occurred frequently. Such accidents not only have a huge impact on the environment but also have serious consequences for people and the economy, such as fires and explosions. Therefore, it is necessary to understand the characteristics and laws of road transport accidents of hazmat systematically. This paper investigated 2777 hazmat transportation accidents in China from 2013 to 2019 to identify the characteristics, consequences, and causes of the accident. The results show that August (10.05%) and December (9.76%) are the peak periods of hazmat transportation accidents, while most hazmat transportation accidents occurred in the early morning (6:00–9:00 a.m.) and at noon (9:00 a.m.–12:00 p.m.) hours. For the geographical location, the accidents mainly occurred in the east China (34.35%) and the northwest China areas (14.87%). The main types of hazmat transportation accidents were rollover (35.36%), rear-end (22.58%), and collision (14.87%), where the probability of a major leak was high. The most common hazmat transportation accidents involve gas (17.79%), flammable liquid (56.07%), and corrosive substance (12.28%). The most common consequences of the hazmat transportation accidents were leakage (80.34%), followed by fire release (8.32%) and explosion release (2.34%). Human factor (26.74%) is the main cause of hazmat transportation accidents. These findings could help hazmat transportation managers and planners develop appropriate measures for improving hazmat transportation safety.