Road Crack Research Articles

Road crack detection using pixel classification and intensity-based distinctive fuzzy C-means clustering

Road crack detection using pixel classification and intensity-based distinctive fuzzy C-means clustering

UTE-CrackNet: transformer-guided and edge feature extraction U-shaped road crack image segmentation

UTE-CrackNet: transformer-guided and edge feature extraction U-shaped road crack image segmentation

Automated crack localization for road safety using contextual u-net with spatial-channel feature integration

Accurate and timely crack localization is crucial for road safety and maintenance, but image processing and hand-crafted feature engineering methods, often fail to distinguish cracks from background noise under diverse lighting and surface conditions. This paper proposes a framework utilizing contextual U-Net deep learning model to automatically localize cracks in road images. The framework design considers crack localization as a task of pixel-level segmenting, and analyzing each pixel in a road image to determine if it belongs to a crack or not. The proposed U-Net model uses a robust EfficientNet encoder to capture crucial details (spatial features) and overall patterns (channel-wise features) within the road image. This balanced approach helps the model learn effectively from both individual elements and the context of the images, leading to improved crack detection. A customized hierarchical attention mechanism is designed to make U-Net model contextually adaptive to focus on relevant features at different scales and resolutions for accurately localizing road cracks that can vary widely in size and shape. The model's effectiveness is demonstrated through extensive evaluations on the benchmarked and custom-made datasets.

A lightweight convolutional neural network for detecting road cracks

A lightweight convolutional neural network for detecting road cracks

A plug-and-play framework for curvilinear structure segmentation based on a learned reconnecting regularization

Curvilinear structures are present in various fields in image processing such as blood vessels in medical imaging or roads in remote sensing. Their detection is crucial for many applications. In this article, we propose an unsupervised plug-and-play framework for the segmentation of curvilinear structures that focuses on the preservation of their connectivity. This framework includes an algorithm for generating realistic pairs of connected/disconnected curvilinear structures and a reconnecting regularization operator that can be learned from a synthetic dataset. Once learned, this regularization operator can be plugged into a variational segmentation scheme and used to segment curvilinear structure images without requiring annotations. We demonstrate the interest of our approach on the segmentation of vascular images both in 2D and 3D and compare its results with classic unsupervised and deep learning-based approach. Comparative evaluations against unsupervised classic and deep learning-based methods highlight the superior performance of our approach, showcasing remarkable improvements in preserving the connectivity of curvilinear structures (approximately 90% in 2D and 70% in 3D). We finally showcase the good generalizability behavior of our approach on two different applications : road cracks and porcine corneal cells segmentations.

DETECTION OF ROAD DAMAGES(CRACKS) IN IRAQ BASED ON MACHINE LEARNING

That research aims to develop road maintenance operations that include the road survey process to determine types of defects, knowing the type of proposed maintenance, solving the problem of reporting road problems, and also seeks to facilitate the process of road maintenance at the lowest possible costs To achieve these goals, a project is being designed that automates road maintenance and uses artificial intelligence to determine kind of defect in the way by using the libraries (tensorflow with Detection Object). The proposed study will reach a number of results, which are identifying the defect, knowing its type, , and conducting some engineering operations and calculations to determine the state of the roadway, as a final output. This study will use a deep learning-based object detection method to identify road cracks under different shooting, weather, and illumination scenarios. This makes it possible to identify any cracks in a very short amount of time and at a low cost. Images will take in different weather conditions, such as the intensity of brightness being high, medium or very low. They will also be taken in foggy, rainy and dark conditions. Pictures are also taken at different distances (such as one meter, half a meter, one and a half meters) to study the behavior of the pictures and the possibility of extracting defects from them in different circumstances.

A Road Crack Segmentation Method Based on Transformer and Multi-Scale Feature Fusion

To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of deep learning technologies has shown tremendous potential in improving the efficiency of road crack detection. While convolutional neural networks have proven effective in most semantic segmentation tasks, overcoming their limitations in road crack segmentation remains a challenge. To address this, this paper proposes a novel road crack segmentation network that leverages the powerful spatial feature modeling capabilities of Swin Transformer and the Encoder–Decoder architecture of DeepLabv3+. Additionally, the incorporation of a multi-scale coding module and attention mechanism enhances the network’s ability to densely fuse multi-scale features and expand the receptive field, thereby improving the integration of information from feature maps. Performance comparisons with current mainstream semantic segmentation models on crack datasets demonstrate that the proposed model achieves the best results, with an MIoU of 81.06%, Precision of 79.95%, and F1-score of 77.56%. The experimental results further highlight the model’s superior ability in identifying complex and irregular cracks and extracting contours, providing guidance for future applications in this field.

How to Make a State of the Art Report—Case Study—Image-Based Road Crack Detection: A Scientometric Literature Review

With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long studied image-based road crack detection techniques, especially the deep learning methods that have emerged in recent years, leading to breakthrough developments in the field. However, many issues remain in road crack detection methods using deep learning techniques. The field lacks state-of-the-art systematic reviews that can scientifically and effectively analyze existing works, document research trends, summarize outstanding research results, and identify remaining shortcomings. To conduct a systematic review of the relevant literature, a bibliometric analysis and a critical analysis of the papers published in the field were performed. VOSviewer and CiteSpace text mining tools were used to analyze and visualize the bibliometric analysis of some parameters derived from the articles. The history and current status of research in the field by authors from all over the world are elucidated and future trends are analyzed.

Bituminous mixes using Sisal fibre for controlling the fatigue cracks in rural roads in India

Bituminous mixes using Sisal fibre for controlling the fatigue cracks in rural roads in India

Enhancing Road Crack Localization for Sustainable Road Safety Using HCTNet

Road crack detection is crucial for maintaining and inspecting civil infrastructure, as cracks can pose a potential risk for sustainable road safety. Traditional methods for pavement crack detection are labour-intensive and time-consuming. In recent years, computer vision approaches have shown encouraging results in automating crack localization. However, the classical convolutional neural network (CNN)-based approach lacks global attention to the spatial features. To improve the crack localization in the road, we designed a vision transformer (ViT) and convolutional neural networks (CNNs)-based encoder and decoder. In addition, a gated-attention module in the decoder is designed to focus on the upsampling process. Furthermore, we proposed a hybrid loss function using binary cross-entropy and Dice loss to evaluate the model’s effectiveness. Our method achieved a recall, F1-score, and IoU of 98.54%, 98.07%, and 98.72% and 98.27%, 98.69%, and 98.76% on the Crack500 and Crack datasets, respectively. Meanwhile, on the proposed dataset, these figures were 96.89%, 97.20%, and 97.36%.

Detection of Road Crack Images Based on Multistage Feature Fusion and a Texture Awareness Method.

Structural health monitoring for roads is an important task that supports inspection of transportation infrastructure. This paper explores deep learning techniques for crack detection in road images and proposes an automatic pixel-level semantic road crack image segmentation method based on a Swin transformer. This method employs Swin-T as the backbone network to extract feature information from crack images at various levels and utilizes the texture unit to extract the texture and edge characteristic information of cracks. The refinement attention module (RAM) and panoramic feature module (PFM) then merge these diverse features, ultimately refining the segmentation results. This method is called FetNet. We collect four public real-world datasets and conduct extensive experiments, comparing FetNet with various deep-learning methods. FetNet achieves the highest precision of 90.4%, a recall of 85.3%, an F1 score of 87.9%, and a mean intersection over union of 78.6% on the Crack500 dataset. The experimental results show that the FetNet approach surpasses other advanced models in terms of crack segmentation accuracy and exhibits excellent generalizability for use in complex scenes.

GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion

Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone’s perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research.

Cultivating road safety: A comprehensive examination of intelligent ensemble-based road crack detection

Cultivating road safety: A comprehensive examination of intelligent ensemble-based road crack detection

ERNet: A Rapid Road Crack Detection Method Using Low-Altitude UAV Remote Sensing Images

The rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently, this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can provide images with a centimeter-level or even subcentimeter-level ground resolution, which provides a new, efficient, and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we proposed a real-time edge reconstruction crack detection network (ERNet) that adopted multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and the background. To capture global dependencies across spatial and channel levels, we proposed an efficient bilateral decomposed convolutional attention module (BDAM) that combined depth-separable convolution and dilated convolution to capture global dependencies across the spatial and channel levels. To enhance the accuracy of crack detection, we used a coordinate-based fusion module that integrated spatial, semantic, and edge reconstruction information. In addition, we proposed an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. The experimental results demonstrated that our network achieved the best balance between accuracy and inference speed compared to six established models.

AHC-Net: a road crack segmentation network based on dual attention mechanism and multi-feature fusion

AHC-Net: a road crack segmentation network based on dual attention mechanism and multi-feature fusion

Analyzing Joshimath’s sinking: causes, consequences, and future prospects with remote sensing techniques

The Himalayas are highly susceptible to various natural disasters, such as the tectonically induced land deformation, earthquakes, landslides, and extreme climatic events. Recently, the Joshimath town witnessed a significantly large land subsidence activity. The phenomenon resulted in the development of large cracks in roads and in over 868 civil structures, posing a significant risk to inhabitants and infrastructure of the area. This study uses a time-series synthetic aperture radar (SAR) interferometry-based PSInSAR approach to monitor land deformation utilizing multi-temporal Sentinel-1 datasets. The line of sight (LOS) land deformation velocity for the Joshimath region, calculated for the year 2022–2023 using a PSInSAR-based approach, varies from − 89.326 to + 94.46 mm/year. The + ve sign indicates the LOS velocity/displacement away from the SAR sensor, whereas − ve sign signifies the earth's movement towards the SAR sensor in the direction of LOS. In addition, the study investigates feature tracking land displacement analysis using multi-temporal high-resolution Planet datasets. The result of this analysis is consistent with the PSInSAR results. The study also estimated the land deformation for the periods 2016–2017, 2018–2019, and 2020–2021 separately. Our results show that the Joshimath region experienced the highest land deformation during the year 2022–2023. During this period, the maximum land subsidence was observed in the north-western part of the town. The maximum LOS land deformation velocity + 60.45 mm/year to + 94.46 mm/year (2022–2023), occurred around Singhdwar, whereas the north and central region of the Joshimath town experienced moderate to high subsidence of the order of + 10.45 mm/year to + 60.45 mm/year (2022–2023), whereas the south-west part experienced an expansion of the order of 84.65 mm/year to − 13.13 mm/year (2022–2023). Towards the south-east, the town experienced rapid land subsidence, − 13.13 mm/year to − 5 mm/year (2022–2023). The study analyzes the causative factors of the observed land deformation in the region. Furthermore, this work assesses the ground conditions of the Joshimath region using UAV datasets acquired in the most critically affected areas such as Singhdhaar, Hotel Mountain View, Malhari Hotel, and Manoharbagh. Finally, the study provides recommendations and future prospects for the development policies that need to be adopted in the critical Himalayan regions susceptible to land deformation. The study suggests that land deformation in the region is primarily attributed to uncontrolled anthropogenic activities, infrastructural development, along with inadequate drainage systems.

PMENet: a parallel UNet based on the fusion of multiple attention mechanisms for road crack segmentation

PMENet: a parallel UNet based on the fusion of multiple attention mechanisms for road crack segmentation

Deep learning approaches for autonomous crack detection in concrete wall, brick deck and pavement

Detecting cracks is vital for inspecting and maintaining concrete structures, enabling early intervention and preventing potential damage. The advent of computer vision and image processing in civil engineering has ushered in deep learning-based semi-automatic/automatic techniques, replacing traditional visual inspections. These methods, driven by autonomous diagnosis, have applications across various sectors, fostering rapid progress in civil engineering. In this study, we present an approach that combines vision transformers and convolutional neural networks (CNN) for autonomously diagnosing cracks in bridges, roads, and walls. Performance enhancement was achieved through transfer learning, data augmentation, and optimized hyperparameters, utilizing popular CNN and ViT architectures. The proposed method was tested on the SDNET2018 dataset, comprising over 56,000 images. Experimental results demonstrated the approach's effectiveness, achieving high accuracy in detecting road cracks at 96.41%, wall cracks at 92.76%, and bridge cracks at 92.81%. These findings highlight the promising potential of deep learning in this field.

Concrete Highway Crack Detection Based on Visible Light and Infrared Silicate Spectrum Image Fusion.

Cracks provide the earliest and most immediate visual response to structural deterioration of asphalt pavements. Most of the current methods for crack detection are based on visible light sensors and convolutional neural networks. However, such an approach obviously limits the detection to daytime and good lighting conditions. Therefore, this paper proposes a crack detection technique cross-modal feature alignment of YOLOV5 based on visible and infrared images. The infrared spectrum characteristics of silicate concrete can be an important supplement. The adaptive illumination-aware weight generation module is introduced to compute illumination probability to guide the training of the fusion network. In order to alleviate the problem of weak alignment of the multi-scale feature map, the FA-BIFPN feature pyramid module is proposed. The parallel structure of a dual backbone network takes 40% less time to train than a single backbone network. As determined through validation on FLIR, LLVIP, and VEDAI bimodal datasets, the fused images have more stable performance compared to the visible images. In addition, the detector proposed in this paper surpasses the current advanced YOLOV5 unimodal detector and CFT cross-modal fusion module. In the publicly available bimodal road crack dataset, our method is able to detect cracks of 5 pixels with 98.3% accuracy under weak illumination.

A lightweight road crack detection algorithm based on improved YOLOv7 model

A lightweight road crack detection algorithm based on improved YOLOv7 model