Road Crack Research Articles

PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods

Road infrastructure is a critical component of modern society, with its maintenance directly influencing traffic safety and logistical efficiency. In this context, automated crack detection technology plays a vital role in reducing maintenance costs and enhancing operational efficiency. However, previous studies are limited by the fact that they provide only bounding box or segmentation mask annotations for a restricted number of crack classes and use a relatively small size of datasets. To address these limitations and advance deep learning-based crack segmentation, this study introduces a novel crack segmentation dataset that reflects real-world road conditions. The proposed dataset includes various types of cracks and defects—such as slippage, rutting, and construction-related cracks—and provides polygon-based segmentation masks captured from an egocentric, vehicle-mounted perspective. Using this dataset, we evaluated the performance of semantic and instance segmentation models. Notably, SegFormer achieved the highest Pixel Accuracy (PA) and mean Intersection over Union (mIoU) for semantic segmentation, while YOLOv7 exhibited outstanding detection performance for alligator crack class, recording an AP50 of 87.2% and AP of 57.5%. In contrast, all models struggled with the reflection crack type, indicating the inherent segmentation challenges. Overall, this study provides a practical and robust foundation for future research in automated road crack segmentation. Additional resources including the dataset and annotation details can be found at our GitHub repository.

Road crack detection based on improved RT-DETR

Road crack detection based on improved RT-DETR

AFQSeg: An Adaptive Feature Quantization Network for Instance-Level Surface Crack Segmentation

Concrete surface crack detection plays a crucial role in infrastructure maintenance and safety. Deep learning-based methods have shown great potential in this task. However, under real-world conditions such as poor image quality, environmental interference, and complex crack patterns, existing models still face challenges in detecting fine cracks and often rely on large training parameters, limiting their practicality in complex environments. To address these issues, this paper proposes a crack detection model based on adaptive feature quantization, which primarily consists of a maximum soft pooling module, an adaptive crack feature quantization module, and a trainable crack post-processing module. Specifically, the maximum soft pooling module improves the continuity and integrity of detected cracks. The adaptive crack feature quantization module enhances the contrast between cracks and background features and strengthens the model’s focus on critical regions through spatial feature fusion. The trainable crack post-processing module incorporates edge-guided post-processing algorithms to correct false predictions and refine segmentation results. Experiments conducted on the Crack500 Road Crack Dataset show that, the proposed model achieves notable improvements in detection accuracy and efficiency, with an average F1-score improvement of 2.81% and a precision gain of 2.20% over the baseline methods. In addition, the model significantly reduces computational cost, achieving a 78.5–88.7% reduction in parameter size and up to 96.8% improvement in inference speed, making it more efficient and deployable for real-world crack detection applications.

A Review Study of Intelligent Road Crack Detection: Algorithms and Systems

A Review Study of Intelligent Road Crack Detection: Algorithms and Systems

Advancing road safety: A lightweight feature fusion model for robust road crack segmentation

Advancing road safety: A lightweight feature fusion model for robust road crack segmentation

A Multi-Scale Hybrid Segmentation and MRMR Feature Selection Approach for Road Crack Detection Using XGBoost Classifier

A Multi-Scale Hybrid Segmentation and MRMR Feature Selection Approach for Road Crack Detection Using XGBoost Classifier

LiDAR-Based Road Cracking Detection: Machine Learning Comparison, Intensity Normalization, and Open-Source WebGIS for Infrastructure Maintenance

This study introduces an innovative and scalable approach for automated road surface assessment by integrating Mobile Mapping System (MMS)-based LiDAR data analysis with an open-source WebGIS platform. In a U.S.-based case study, over 20 datasets were collected along Interstate I-65 in West Lafayette, Indiana, using the Purdue Wheel-based Mobile Mapping System—Ultra High Accuracy (PWMMS-UHA), following Indiana Department of Transportation (INDOT) guidelines. Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. Results demonstrate that RF consistently outperformed DBSCAN, particularly under intensity normalization, achieving Jaccard Index values of 94% for longitudinal and 88% for transverse cracks. A key contribution of this work is the integration of geospatial analytics into an interactive, open-source WebGIS environment—developed using Blender, QGIS, and Lizmap—to support predictive maintenance planning. Moreover, intervention thresholds were defined based on crack surface area, aligned with the Pavement Condition Index (PCI) and FHWA standards, offering a data-driven framework for infrastructure monitoring. This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources.

Road crack detection method based on lightweight YOLOv8-LUAPD and UAV images

Road crack detection method based on lightweight YOLOv8-LUAPD and UAV images

An Automated and Integrated Sensing System for Road Monitoring using UAV Images and an Optimized R-CNN

One of the most relevant, but at the same time most time-consuming and costly, aspects of the infrastructure system is the monitoring of road infrastructures, often subject to deterioration that compromises their use. Current monitoring systems consist of individual reports or the use of human resources that, through equipped vehicles, have the purpose of carrying out a reconnaissance process, which is often characterized by errors and uncertainties. In this context, the aim of this work was to experiment and implement an experimental and innovative Automated and Integrated Sensing System (AISS) for the monitoring of road infrastructures. This system, starting from Remote Sensing images from Unmanned Aerial Vehicles (UAVs), uses a Mask R-CNN neural network to identify road cracks. This information, together with other information, is included in a database, which is then used in a Geographical Information System (GIS) for relative visualization. This work therefore proposes a methodology for the implementation of a system that helps policy makers in determining the most urgent interventions. In fact, a categorization of the severity of degradation and a user-friendly visualization, allow us to make decisions based on data.

Segmentation detection method in tree-shaded environment for road cracks collected by inspection vehicle on WFU-Unet

Road cracks pose a significant safety hazard to transportation, making timely detection crucial for traffic safety. Traditional crack segmentation methods face three main issues: (1) Tree shadow background affects crack recognition in real-world environments. (2) Conventional convolutional neural networks fail to detect complete cracks. (3) Direct deconvolution during upsampling results in unclear crack details. To address these challenges, this paper proposes the WFU-Unet model for road crack detection and segmentation. First, the WCM module, constructed with wavelet transform, ConvNext, and MobileNet, reduces shadow interference, enabling the network to distinguish between cracks and tree shadows. Second, the Fuse module replaces traditional convolutional blocks, enhancing the network’s ability to extract crack features. Finally, the Up module substitutes conventional upsampling techniques to minimize spatial information loss of cracks. Experimental results show that the WFU-Unet model achieves a Miou of 81.68%, precision of 91.43%, recall of 86.63%, and F1-Score of 88.93%. Compared to other models, WFU-Unet demonstrates superior generalization ability and segmentation accuracy, making it more suitable for crack detection in shadowed environments.

A rapid and precise detection and measurement method for road cracks based on video keyframes and DPPNet

A rapid and precise detection and measurement method for road cracks based on video keyframes and DPPNet

Road crack detection of drone-captured images based on TPH-YOLOv5

ABSTRACT Pavement cracks are common indicators of road distress. Road maintenance agencies must monitor conditions and detect cracks promptly to reduce costs, ensure structural reliability, and enhance safety and comfort for drivers. Road crack detection is typically performed using road inspection vehicles with human drivers, but this approach is inefficient for large-scale road networks, consuming significant manpower and time. Utilising unmanned aerial vehicles (UAVs) for crack detection can save time and manpower without disrupting traffic flow. However, images of cracks captured by UAVs often have complex backgrounds and unevenly distributed objects, which pose challenges for existing models in achieving the required accuracy in real road environments. The Transformer Prediction Heads-YOLOv5 (TPH-YOLOv5) model is employed to address these issues in this paper. It improves the YOLOv5 model by adding a prediction head for better crack detection across different sizes. The original detection head is upgraded to TPH integrated with a convolutional neural network for multi-scale feature fusion. This design enhances the model's detection capability for cracks of varying sizes and improves robustness. Additionally, the model incorporates the Convolutional Block Attention Model (CBAM) for adaptive channel and spatial attention, enhancing performance in identifying road cracks. By employing data augmentation, multi-scale testing, model integration, and additional classifiers, the model achieves superior performance. Experimental results on the RDD2022 China_Drone dataset demonstrate improved accuracy, recall, and mAP metrics compared with YOLOv5, meeting engineering application requirements.

Impact of Image Preprocessing and Crack Type Distribution on YOLOv8-Based Road Crack Detection.

Road crack detection is crucial for ensuring pavement safety and optimizing maintenance strategies. This study investigated the impact of image preprocessing methods and dataset balance on the performance of YOLOv8s-based crack detection. Four datasets (CFD, Crack500, CrackTree200, and CrackVariety) were evaluated using three image formats: RGB, grayscale (five conversion methods), and binarized images. The experimental results indicate that RGB images consistently achieved the highest detection accuracy, confirming that preserving color-based contrast and texture information benefits YOLOv8's feature extraction. Grayscale conversion showed dataset-dependent variations, with different methods performing best on different datasets, while binarization generally degraded detection accuracy, except in the balanced CrackVariety dataset. Furthermore, this study highlights that dataset balance significantly impacts model performance, as imbalanced datasets (CFD, Crack500, CrackTree200) led to biased predictions favoring dominant crack classes. In contrast, CrackVariety's balanced distribution resulted in more stable and generalized detection. These findings suggest that dataset balance has a greater influence on detection accuracy than preprocessing methods. Future research should focus on data augmentation and resampling strategies to mitigate class imbalance, as well as explore multi-modal fusion approaches for further performance enhancements.

ANF-Net: A Refined Segmentation Network for Road Scenes with Multiple Noises and Various Morphologies of Cracks

Cracks are a common early road defect that tends to worsen with the aging of roads, potentially leading to severe structural damage. Timely and accurate crack detection plays a crucial role in mitigating such risks and holds significant importance for infrastructure maintenance. Deep learning techniques have demonstrated excellent performance in image-based crack extraction tasks. However, challenges persist due to the presence of numerous noisy pixels in the image background and the diverse and intricate morphologies of cracks, leading to issues such as misclassification and omission. To address these issues, this paper proposes a refined pixel-level segmentation network (ANF-Net) suitable for complex crack detection scenarios with high noise levels and diverse crack morphologies. When extracting crack features, on one hand, the network introduces an attention module tailored for crack scenes to learn pixel-wise feature weights, enabling the network to focus on crack regions and thereby reducing the impact of similar background features, mitigating false positives caused by noise misclassification. On the other hand, a constrained multi-morphological convolution structure is constructed by imposing learnable continuous constraints on the deformation offsets of convolutional kernels, allowing the network to adaptively fit different crack shapes. This design enhances the network’s ability to extract cracks in morphologically diverse, narrow, and densely populated regions, effectively preventing issues such as crack extraction interruptions and omissions. Additionally, a multi-scale discrete wavelet transform enhancement module is designed to assist the network in considering frequency domain information that contains crack features, further improving its feature extraction capability. Simulations are conducted using three publicly available crack datasets, and the proposed method is compared with mainstream segmentation models. The results demonstrate that the proposed method achieves F1 scores of 87.9%, 82.5%, and 71.5% on the three datasets, respectively, all of which surpass the performance of current mainstream segmentation models. The proposed network accurately extracts road cracks and exhibits robust performance.

A Novel Fuzzy C-Means Clustering Framework for Accurate Road Crack Detection: Incorporating Pixel Augmentation and Intensity Difference Features

A Novel Fuzzy C-Means Clustering Framework for Accurate Road Crack Detection: Incorporating Pixel Augmentation and Intensity Difference Features

Multiscene Auxiliary Network-Based Road Crack Detection Under the Framework of Distributed Edge Intelligence

Multiscene Auxiliary Network-Based Road Crack Detection Under the Framework of Distributed Edge Intelligence

Algorithm for pixel-level concrete pavement crack segmentation based on an improved U-Net model

Cracks that occur in concrete surfaces are numerous and diverse, and different cracks will affect road safety in different degrees. Accurately identifying pavement cracks is crucial for assessing road conditions and formulating maintenance strategies. This study improves the original U-shaped convolutional network (U-Net) model through the introduction of two innovations, thereby modifying its structure, reducing the number of parameters, enhancing its ability to distinguish between background and cracks, and improving its speed and accuracy in crack detection tasks. Additionally, datasets with different exposure levels and noise conditions are used to train the network, broadening its predictive ability. A custom dataset of 960 road crack images was added to the public dataset to train and evaluate the model. The test results demonstrate that the proposed U-Net-FML model achieves high accuracy and detection speed in complex environments, with MIoU, F1 score, precision, and recall values of 76.4%, 74.2%, 84.2%, and 66.4%, respectively, significantly surpassing those of the other models. Among the seven comparison models, U-Net-FML has the strongest overall performance, highlighting its engineering value for precise detection and efficient analysis of cracks.

An optimized and precise road crack segmentation network in complex scenarios

AbstractRoad cracks pose a serious threat to the stability of road structures and traffic safety. Therefore, this paper proposes an optimized accurate road crack segmentation network called MBGBNet, which can solve the problems of complex background, tiny cracks, and irregular edges in road segmentation. First, multi‐scale domain feature aggregation is proposed to address the interference of complex background. Second, bidirectional embedding fusion adaptive attention is proposed to capture the features of tiny cracks, and finally, Gaussian weighted edge segmentation algorithm is proposed to ensure the accuracy of crack edge segmentation. In addition, this paper uses the preheated bat optimization algorithm, which can quickly determine the optimal learning rate to converge the equilibrium. In the validation experiments on the self‐built dataset, mean intersection over union reaches 80.54% and precision of 86.38%. MBGBNet outperforms the other seven state‐of‐the‐art crack segmentation networks on the three classical crack datasets, highlighting its advanced segmentation capabilities. Therefore, MBGBNet is an effective auxiliary method for solving road safety problems.

Efficient and accurate road crack detection technology based on YOLOv8-ES

Road damage detection is an important aspect of road maintenance. Traditional manual inspections are laborious and imprecise. With the rise of deep learning technology, pavement detection methods employing deep neural networks give an efficient and accurate solution. However, due to background diversity, limited resolution, and fracture similarity, it is tough to detect road cracks with high accuracy. In this study, we offer a unique, efficient and accurate road crack damage detection, namely YOLOv8-ES. We present a novel dynamic convolutional layer(EDCM) that successfully increases the feature extraction capabilities for small fractures. At the same time, we also present a new attention mechanism (SGAM). It can effectively retain crucial information and increase the network feature extraction capacity. The Wise-IoU technique contains a dynamic, non-monotonic focusing mechanism designed to return to the goal-bounding box more precisely, especially for low-quality samples. We validate our method on both RDD2022 and VOC2007 datasets. The experimental results suggest that YOLOv8-ES performs well. This unique approach provides great support for the development of intelligent road maintenance systems and is projected to achieve further advances in future applications.

TRIB crack dataset: automatic recognition system for road cracks detection

Road traffic infrastructure in a city is as essential as veins in the human body. In Romania, road infrastructure is classified based on traffic intensity into four types of streets: magistral (used for crossing the city), connection, collection, and local use. This study utilizes the TRIB crack dataset (Traffic Road Infrastructure from Bucharest crack dataset), which consists of high-quality images of various types of road cracks. The dataset can be effectively used for different computer vision tasks, such as classification, object detection, and more. To meet the diverse requirements of deep learning methods, the dataset includes images capturing different types of road cracks, such as longitudinal, transverse, block, and alligator cracks, as well as various artifacts like oil stains, road markings on asphalt, leaves, and more. The images were taken from a height of 100 centimeters above the road surface, resulting in a dataset of 137 RGB (red, green, blue) images. To make the images suitable for deep learning methods, they were divided into smaller images with a resolution of 256 × 256 pixels. Additionally, various image augmentation techniques were applied. During the splitting process, some images contained no cracks, while others included cracks. This resulted in the creation of two distinct subsets: one containing image with road cracks and another with images without cracks.