Search Results (85)

The subgrade is a critical component of highway infrastructure that directly affects pavement performance and traffic safety. With the rapid expansion of highway networks and increasing heavy-truck traffic, latent subgrade distresses, such as insufficient base strength, uneven settlement, and base cracking, have become key factors limiting pavement serviceability. These distresses are often difficult to detect at early stages and may evolve into sudden structural failures if not properly identified. This study investigates the evolution mechanisms and spatial characteristics of representative subgrade distresses through an integrated framework combining FWD screening, GPR imaging, core sampling, and Abaqus-based finite element simulation. Field data were collected from the Changshen Expressway. Potential weak zones were first identified using FWD testing and further localized by GPR, while multilayer constitutive parameters were obtained from core sample analyses. The field-derived material parameters were then incorporated into an FE model to simulate pavement responses under loading and to interpret the underlying distress mechanisms. The proposed framework enables identification of dominant distress types, quantification of stiffness degradation, and clarification of deterioration pathways within the subgrade system. The results provide practical support for condition assessment, health monitoring, and maintenance decision-making in highway infrastructure. Full article

The prompt and precise detection of road damage is vital for effective infrastructure management, forming the foundation for intelligent transportation systems and cost-effective pavement maintenance. While current convolutional neural network (CNN)-based methodologies have made progress, they are fundamentally limited by treating damages as independent, isolated entities, thereby ignoring the intrinsic spatial symmetry and topological organization inherent in complex damage patterns like alligator cracking. This conceptual asymmetry in modeling leads to two major deficiencies: “context blindness,” which overlooks essential structural interrelations, and “temporal inconsistency” in video analysis, resulting in unstable, flickering predictions. To address this, we propose a Spatio-Temporal Graph Mamba You-Only-Look-Once (STG-Mamba-YOLO) network, a novel architecture that introduces a symmetry-informed, hierarchical reasoning process. Our approach explicitly models and integrates contextual dependencies across three levels to restore a holistic and consistent structural representation. First, at the pixel level, a Mamba state-space model within the YOLO backbone enhances the modeling of long-range spatial dependencies, capturing the elongated symmetry of linear cracks. Second, at the object level, an intra-frame damage Graph Network enables explicit reasoning over the topological symmetry among damage candidates, effectively reducing false positives by leveraging their relational structure. Third, at the sequence level, a Temporal Graph Mamba module tracks the evolution of this damage graph, enforcing temporal symmetry across frames to ensure stable, non-flickering results in video streams. Comprehensive evaluations on multiple public benchmarks demonstrate that our method outperforms existing state-of-the-art approaches. STG-Mamba-YOLO shows significant advantages in identifying intricate damage topologies while ensuring robust temporal stability, thereby validating the effectiveness of our symmetry-guided, multi-level contextual fusion paradigm for structural health monitoring. Full article

Pavement condition assessment using computer vision has emerged as an efficient alternative to traditional manual surveys, which are often labor-intensive and time-consuming. Leveraging deep learning, pavement distress such as cracks can be automatically detected, segmented, and quantified from high-resolution images captured by survey vehicles. Although numerous segmentation models have been proposed to generate crack masks, they typically require extensive pixel-level annotations, leading to high labeling costs. To overcome this limitation, this study integrates the Segmentation Anything Model (SAM), which produces accurate segmentation masks from simple bounding box prompts while leveraging its zero-shot capability to generalize to unseen images with minimal retraining. However, since SAM alone is not an end-to-end solution, we incorporate YOLOv8 for automated crack detection, eliminating the need for manual box annotation. Furthermore, the framework applies local refinement techniques to enhance mask precision and employs Optical Character Recognition (OCR) to automatically extract embedded GPS coordinates for geospatial mapping. The proposed framework is empirically validated using open-source pavement images from Yamanashi, demonstrating effective automated detection, classification, quantification, and geospatial mapping of pavement cracks. The results support automated pavement distress mapping onto real-world road networks, facilitating efficient maintenance planning for road agencies. Full article

To address the issues of low accuracy and high rates of false detection and missed detection in existing methods for pavement crack identification under complex road conditions, this paper proposes a novel approach named YOLO11-MBC, based on the YOLO11 model. A Multi-scale Feature Fusion Backbone Network (MFFBN) is designed to enhance the model’s capability to recognize and extract crack features in complex environments. Considering that pavement cracks often exhibit elongated topologies and are susceptible to interference from similar features like tree roots or lane markings, we combine the Bidirectional Feature Pyramid Network (BiFPN) with a Multimodal Cross-Attention (MCA) mechanism, constructing a novel BiMCNet to replace the Concat layer in the original network, thereby optimizing the detection of minute cracks. The CGeoCIoU loss function replaces the original CIoU, employing three distinct penalty terms to better reflect the alignment between predicted and ground-truth boxes. The effectiveness of the proposed method is validated through comparative and ablation experiments on the public RDD2022 dataset. Results demonstrate the following: (1) Compared to the baseline YOLO11, YOLO11-MBC achieves a 22.5% improvement in F1-score and an 8% increase in mAP50 by integrating the three proposed modules, significantly enhancing performance for complex pavement crack detection. (2) The improved algorithm demonstrates superior performance. Compared to YOLOv8, YOLOv10, and YOLO11, it achieves precision, recall, F1-score, mAP50, and mAP50-95 of 61%, 70%, 72%, 75%, and 66%, respectively, validating the correctness of our approach. Full article

Ensuring the structural integrity of road infrastructure is vital for transportation safety and long-term sustainability. This study presents a lightweight and accurate pavement crack detection framework named SpcNet, which integrates a Sparse Encoding Module, ConvNeXt V2-based decoder, and a Binary Attention Module (BAM) within an asymmetric encoder–decoder architecture. The proposed method first applies a random masking strategy to generate sparse pixel inputs and employs sparse convolution to enhance computational efficiency. A ConvNeXt V2 decoder with Global Response Normalization (GRN) and GELU activation further stabilizes feature extraction, while the BAM, in conjunction with Channel and Spatial Attention Bridge (CAB/SAB) modules, strengthens global dependency modeling and multi-scale feature fusion. Comprehensive experiments on four public datasets demonstrate that SpcNet achieves state-of-the-art performance with significantly fewer parameters and lower computational cost. On the Crack500 dataset, the method achieves a precision of 91.0%, recall of 85.1%, F1 score of 88.0%, and mIoU of 79.8%, surpassing existing deep-learning-based approaches. These results confirm that SpcNet effectively balances detection accuracy and efficiency, making it well-suited for real-world pavement condition monitoring. Full article

Airport pavement condition assessment plays a critical role in ensuring operational safety, surface functionality, and long-term infrastructure sustainability. Traditional visual inspection methods, although widely used, are increasingly challenged by limitations in accuracy, subjectivity, and scalability. In response, the field has seen a growing adoption of automated and intelligent inspection technologies, incorporating tools such as unmanned aerial vehicles (UAVs), Laser Crack Measurement Systems (LCMS), and machine learning algorithms. This systematic review aims to identify, categorize, and analyze the main technological approaches applied to functional pavement inspections, with a particular focus on surface distress detection. The study examines data collection techniques, processing methods, and validation procedures used in assessing both flexible and rigid airport pavements. Special emphasis is placed on the precision, applicability, and robustness of automated systems in comparison to traditional approaches. The reviewed literature reveals a consistent trend toward greater accuracy and efficiency in systems that integrate deep learning, photogrammetry, and predictive modeling. However, the absence of standardized validation protocols and statistically robust datasets continues to hinder comparability and broader implementation. By mapping existing technologies, identifying methodological gaps, and proposing strategic research directions, this review provides a comprehensive foundation for the development of scalable, data-driven airport pavement management systems. Full article

Using deep learning in automated pavement distress detection has shown huge improvements for transport infrastructure, but a noticeable challenge remains in distinguishing sealed cracks from active ones, which are more evident in high-resolution aerial imagery of airport pavements. Misclassifying sealed cracks, an indicator of maintenance intervention, as structural distress leads to false positives that cause overestimation in distress metrics and, ultimately, inaccurate Pavement Condition Index (PCI) scores. This study tries to address this limitation by investigating whether explicitly labeling sealed cracks as a separate class during training can improve model performance. In this regard, aerial orthophotos of taxiways from one selected airport, as a case study, were collected via Unmanned aerial vehicle (UAV) surveys, and three instance segmentation models based on YOLOv11 (version 11 from You Only Look Once family) were trained on different datasets: one excluding sealed cracks (including only longitudinal and transvers cracks), one including sealed cracks without explicit labeling, and one treating sealed cracks as a separate class. Validation against ground-truth field surveys revealed that the model trained with explicit sealed crack annotations achieved significantly lower error rates, with a 56.7% reduction for longitudinal cracks and a 75.2% reduction for transverse cracks with respect to traditional detection methods. This improvement led to fewer false positives and a more reliable quantification of both longitudinal and transverse cracking. The results demonstrate that tailored annotation strategies, which in this study means distinguishing sealed cracks, substantially improve the accuracy of deep learning models for real-world pavement condition assessment. Full article

Accurate pavement crack detection under adverse weather conditions is essential for road safety and effective pavement maintenance. However, factors such as reduced visibility, background noise, and irregular crack morphology make this task particularly challenging in real-world environments. To address these challenges, we propose CrackNet-Weather, which is a robust and efficient detection method that systematically incorporates three key modules: a Haar Wavelet Downsampling Block (HWDB) for enhanced frequency information preservation, a Strip Pooling Bottleneck Block (SPBB) for multi-scale and context-aware feature fusion, and a Dynamic Sampling Upsampling Block (DSUB) for content-adaptive spatial feature reconstruction. Extensive experiments conducted on a challenging dataset containing both rainy and snowy weather demonstrate that CrackNet-Weather significantly outperforms mainstream baseline models, achieving notable improvements in mean Average Precision, especially for low-contrast, fine, and irregular cracks. Furthermore, our method maintains a favorable balance between detection accuracy and computational complexity, making it well suited for practical road inspection and large-scale deployment. These results confirm the effectiveness and practicality of CrackNet-Weather in addressing the challenges of real-world pavement crack detection under adverse weather conditions. Full article

High-resolution 3D pavement images have become a valuable data source for automated surface distress detection and assessment. However, accurately identifying and segmenting cracks from pavement images remains challenging, due to factors such as low contrast and hair-like thinness. This study investigates key factors affecting segmentation performance and proposes a novel deep learning architecture designed to enhance segmentation robustness under these challenging conditions. The proposed model integrates a multi-resolution feature extraction stream with gated attention mechanisms to improve spatial awareness and selectively fuse information across feature levels. Our extensive experiments on a 3D pavement dataset demonstrated that the proposed method outperformed several state-of-the-art architectures, including FCN, U-Net, DeepLab, DeepCrack, and CrackFormer. Compared with U-Net, it improved F1 from 0.733 to 0.780. The gains were most pronounced on thin cracks, with F1 from 0.531 to 0.626. Our paired t-tests across folds showed the method is statistically better than U-Net and DeepCrack on Recall, IoU, Dice, and F1. These findings highlight the effectiveness of the attention-guided, multi-scale feature fusion method for robust crack segmentation using 3D pavement data. Full article

To address the current issue of large computations and the difficulty in balancing model complexity and detection accuracy in pavement defect detection models, a lightweight pavement defect detection algorithm, PGS-YOLO, is proposed based on YOLOv8, which integrates perception enhancement and feature optimization. The algorithm first designs the Receptive-Field Convolutional Block Attention Module Convolution (RFCBAMConv) and the Receptive-Field Convolutional Block Attention Module C2f-RFCBAM, based on which we construct an efficient Perception Enhanced Feature Extraction Network (PEFNet) that enhances multi-scale feature extraction capability by dynamically adjusting the receptive field. Secondly, the dynamic upsampling module DySample is introduced into the efficient feature pyramid, constructing a new feature fusion pyramid (Generalized Dynamic Sampling Feature Pyramid Network, GDSFPN) to optimize the multi-scale feature fusion effect. In addition, a shared detail-enhanced convolution lightweight detection head (SDCLD) was designed, which significantly reduces the model’s parameters and computation while improving localization and classification performance. Finally, Wise-IoU was introduced to optimize the training performance and detection accuracy of the model. Experimental results show that PGS-YOLO increases mAP50 by 2.8% and 2.9% on the complete GRDDC2022 dataset and the Chinese subset, respectively, outperforming the other detection models. The number of parameters and computations are reduced by 10.3% and 9.9%, respectively, compared to the YOLOv8n model, with an average frame rate of 69 frames per second, offering good real-time performance. In addition, on the CRACK500 dataset, PGS-YOLO improved mAP50 by 2.3%, achieving a better balance between model complexity and detection accuracy. Full article

Crack detection in complex pavement scenarios remains challenging due to the sparse small-target features and computational inefficiency of existing methods. To address these limitations, this study proposes an enhanced architecture based on Mask2Former. The framework integrates two key innovations. A Local Perception Module (LPM) reconstructs geometric topological relationships through a Sequence-Space Dynamic Transformation Mechanism (DS2M), enhancing neighborhood feature extraction via depthwise separable convolutions. Simultaneously, an Auxiliary Convolutional Layer (ACL) combines lightweight residual convolutions with shallow high-resolution features, preserving critical edge details through channel attention weighting. Experimental evaluations demonstrate the model’s superior performance, achieving improvements of 3.2% in mIoU and 2.7% in mAcc compared to baseline methods, while maintaining computational efficiency with only 12.8 GFLOPs. These results validate the effectiveness of geometric relationship modeling and hierarchical feature fusion for pavement crack detection, suggesting practical potential for infrastructure maintenance systems. The proposed approach balances precision and efficiency, offering a viable solution for real-world applications with complex crack patterns and hardware constraints. Full article

Efficient and accurate recognition of highway pavement cracks is crucial for the timely maintenance and long-term use of expressways. Among the existing crack acquisition methods, human-based approaches are inefficient, whereas carrier-based automated methods are expensive. Additionally, both methods present challenges related to traffic obstruction and safety risks. To address these challenges, we propose a fixed pan-tilt-zoom (PTZ) vision-based highway pavement crack recognition workflow. Pavement cracks often exhibit complex textures with blurred boundaries, low contrast, and discontinuous pixels, leading to missed and false detection. To mitigate these issues, we introduce an algorithm named contrast-enhanced feature reconstruction (CEFR), which consists of three parts: comparison-based pixel transformation, nonlinear stretching, and generating a saliency map. CEFR is an image pre-processing algorithm that enhances crack edges and establishes uniform inner-crack characteristics, thereby increasing the contrast between cracks and the background. Extensive experiments demonstrate that CEFR improves recognition performance, yielding increases of 3.1% in F1-score, 2.6% in mAP@0.5, and 4.6% in mAP@0.5:0.95, compared with the dataset without CEFR. The effectiveness and generalisability of CEFR are validated across multiple models, datasets, and tasks, confirming its applicability for highway maintenance engineering. Full article

Accurate segmentation of pavement cracks from high-resolution remote sensing imagery plays a crucial role in automated road condition assessment and infrastructure maintenance. However, crack structures often exhibit asymmetry, irregular morphology, and multi-scale variations, posing significant challenges to conventional CNN-based methods in real-world environments. Specifically, the proposed ETAFHrNet focuses on two predominant pavement-distress morphologies—linear cracks (transverse and longitudinal) and alligator cracks—and has been empirically validated on their intersections and branching patterns over both asphalt and concrete road surfaces. In this work, we present ETAFHrNet, a novel attention-guided segmentation network designed to address the limitations of traditional architectures in detecting fine-grained and asymmetric patterns. ETAFHrNet integrates Transformer-based global attention and multi-scale hybrid feature fusion, enhancing both contextual perception and detail sensitivity. The network introduces two key modules: the Efficient Hybrid Attention Transformer (EHAT), which captures long-range dependencies, and the Cross-Scale Hybrid Attention Module (CSHAM), which adaptively fuses features across spatial resolutions. To support model training and benchmarking, we also propose QD-Crack, a high-resolution, pixel-level annotated dataset collected from real-world road inspection scenarios. Experimental results show that ETAFHrNet significantly outperforms existing methods—including U-Net, DeepLabv3+, and HRNet—in both segmentation accuracy and generalization ability. These findings demonstrate the effectiveness of interpretable, multi-scale attention architectures in complex object detection and image classification tasks, making our approach relevant for broader applications, such as autonomous driving, remote sensing, and smart infrastructure systems. Full article

Roads are critical infrastructure in multi-sectoral development. Any country that aims to expand and stabilize its activities must have a network of paved roads in good condition. However, that is not the case in many countries. The usual methods of recording and classifying pavement distress on the roads require a lot of equipment, technicians, and time to obtain the nature and indices of the damage to estimate the roadway’s quality level. This study proposes the use of pavement distress detection and classification models based on Convolutional Neural Networks, starting from videos taken of any asphalt road. To carry out this work, various routes were filmed to list the degradations concerned. Images were extracted from these videos and then resized and annotated. Then, these images were used to constitute several databases of road damage, such as longitudinal cracks, alligator cracks, small potholes, and patching. Within an appropriate development environment, three Convolutional Neural Networks were developed and trained on the databases. The accuracy achieved by the different models varies from 94.6% to 97.3%. This accuracy is promising compared to the literature models. This method would make it possible to considerably reduce the financial resources used for each road data campaign. Full article

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. Full article