Search Results (16)

Rock fissure parameters, such as length, width, and density, are essential for analyzing the progressive instability of open-pit mine slopes. Under the combined effects of engineering disturbance, geological conditions, and environmental factors, slope fissures continuously propagate and evolve. However, large variations in fissure scale, complex rock-surface textures, blurred boundaries, and weak micro-fissure features increase the difficulty of intelligent fissure segmentation, identification, and parameter extraction. Consequently, many mining enterprises still rely on manual interpretation, which is time-consuming and susceptible to subjective errors. To address these challenges, this study develops Crack-YOLO, a task-oriented fissure detection and instance-segmentation model based on YOLOv8-Seg. A total of 500 original UAV images were collected from multiple open-pit mines and processed to construct a dataset containing 3600 fissure image patches, including 3240 images for training and 360 images for testing. In Crack-YOLO, selected C2f modules are replaced with contextual semantic enhancement modules (CoT Blocks), and a texture information enhancement module (SM Block) is incorporated to strengthen contextual semantic representation and fine-grained texture-feature extraction. The model achieved segmentation precision, recall, mAP50, and mAP50:95 values of 0.896, 0.787, 0.854, and 0.392, respectively. For object detection, the corresponding values were 0.968, 0.862, 0.959, and 0.773, respectively. The segmentation results were further processed using K3M skeleton extraction and physical-scale calibration to quantitatively extract geometric parameters, including fissure length, equivalent average width, and azimuth. Validation using an image containing seven representative fissures yielded mean absolute errors of 0.016 m, 0.010 m, and 0.90° for fissure length, equivalent average width, and azimuth, respectively, indicating the feasibility of the proposed parameter-quantification workflow. In an application test conducted in a typical open-pit mine scene, the proposed workflow identified 196 fissures within approximately 22 s and quantitatively analyzed their geometric parameters and distribution characteristics. The results indicate that the proposed method has potential for fissure identification and geometric-parameter quantification in open-pit mine slopes and may provide quantitative data support for slope-fissure monitoring and stability analysis. Full article

Mining-induced surface cracks are direct indicators of ground damage caused by underground coal extraction, yet their accurate three-dimensional (3D) characterization remains challenging because most unmanned aerial vehicle (UAV)-based studies are limited to two-dimensional (2D) detection and planar parameter extraction. This study proposes a UAV–terrestrial laser scanning (TLS) fusion framework for measurable 3D characterization of mining-induced surface cracks under real topographic conditions. High-resolution UAV orthomosaics were used to extract crack semantics with a dual residual-attention U-Net (DRA-UNet). The resulting crack masks were skeletonized and vectorized, and a k-dimensional tree (KDTree)-based spatial matching strategy was applied to link crack centerlines with TLS point clouds, thereby constructing semantic crack point clouds. The framework was evaluated at the Huojitu Mine, Shenmu City, China, using approximately 5000 crack samples. DRA-UNet achieved precision, recall, F1-score, and mean intersection over union values of 85.13%, 77.84%, 81.32%, and 70.26%, respectively. Compared with conventional 2D length measurements, the proposed 3D estimation reduced the mean relative error from 4.49% to 2.64%. Width validation at 24 field points yielded a mean relative error of 4.47%. These results show that UAV–TLS fusion can bridge the gap between planar crack detection and true 3D geometric characterization, providing a practical tool for mining-induced ground damage monitoring. Full article

Accurate service performance evaluation of reinforced concrete (RC) beam structures is crucial for ensuring structural safety and guiding maintenance decisions. However, current practice primarily relies on qualitative visual inspections that fail to quantitatively link apparent defects to internal mechanical behavior. To address this, a novel evaluation framework fusing apparent crack features with static and dynamic responses is proposed. A context-aware grid-based deep learning model (CGDL-Crack) is developed that combines transfer learning with skeleton extraction, achieving crack localization with a maximum validation AP of 96.4% under complex backgrounds. Based on large-scale parametric finite element simulations and Sobol global sensitivity analysis, key state indicators—including static reaction forces, modal frequencies, and crack widths—are identified, and an artificial neural network (ANN) surrogate model is constructed to map multi-source monitoring data to material constitutive parameters. Full-process failure tests on 17 RC beams demonstrate that crack width follows bilinear growth and remains sensitive after stiffness indices saturate. The updated FE model accurately predicts ultimate bearing capacity, demonstrating the effectiveness of the proposed framework and its application potential for RC beam-type components in bridge and building engineering. Full article

Post-earthquake bridge crack detection is a critical step in assessing structural safety. Traditional manual detection of bridge cracks is time-consuming, labor-intensive, and poses significant risks. This paper focuses on the automatic identification of structural cracks by analyzing their morphology, orientation, and distribution characteristics, and preliminarily distinguishes them from non-structural damages such as surface stains and coating peeling. Therefore, this paper proposes a bridge crack recognition algorithm based on image processing. First, the input crack image undergoes preprocessing to obtain a binary image, reducing measurement errors caused by environmental factors or uneven illumination, using an improved Retinex algorithm to enhance image brightness. Second, an improved wavelet transform method is employed to remove large-area noise. Then, connected component analysis is used to filter out point-like and patch-like noise, resulting in a complete and clear crack skeleton. Finally, the crack length, width, and other characteristic values are obtained using an image pixel coordinate calculation method, achieving non-contact, non-destructive measurement of concrete surface crack characteristics. The algorithm is based on two-dimensional image processing and does not directly measure crack depth, but the extracted parameters such as length, width, and area ratio provide important surface-based evidence for rapid post-earthquake bridge structural safety assessment. Multiple experimental results show that the proposed algorithm has a maximum width measurement relative error of less than 2.3%, a length measurement relative error within 8%, and an average peak signal-to-noise ratio (PSNR) of the denoised image increased to 74.73 dB. This algorithm provides an effective automated detection tool for rapid post-earthquake bridge safety assessment. Full article

Intelligent crack detection and displacement measurement are critical for evaluating the health status of dams. Earth-fill dams, composed of fragmented independent material particles, are particularly vulnerable to climate changes that can exacerbate cracking and displacement. Existing crack segmentation methods often suffer from discontinuous crack segmentation and misidentification due to complex background noise. Furthermore, current skeleton line-based width measurement techniques demonstrate limited accuracy in processing complex crack patterns. To address these limitations, this study introduces a novel three-step approach for crack detection in earth-fill dams. Firstly, an enhanced YOLOv8-CGA crack segmentation method is proposed, incorporating a Cascaded Group Attention (CGA) mechanism into YOLOv8 to improve feature diversity and computational efficiency. Secondly, image processing techniques are applied to extract sub-pixel crack edges and skeletons from the segmented regions. Finally, an adaptive skeleton fitting algorithm is developed to achieve high-precision crack width estimation. This approach effectively integrates the pattern recognition capabilities of deep learning with the detailed delineation strengths of traditional image processing. Additionally, dam crest displacements and crack zone strain field are measured via the digital image correlation (DIC) method. The efficacy and robustness of the proposed method are validated through laboratory experiments on an earth-fill dam model, demonstrating its potential for practical structural health monitoring (SHM) applications in a changing climate. Full article

This paper presents a UAV-based building crack real-time detection system that integrates an improved YOLOv8 algorithm with Linear Active Disturbance Rejection Control (LADRC). The system is equipped with a high-resolution camera and sensors to capture high-definition images and height information. First, a trajectory tracking controller based on LADRC was designed for the UAV, which uses a linear extended state observer to estimate and compensate for unknown disturbances such as wind interference, significantly enhancing the flight stability of the UAV in complex environments and ensuring stable crack image acquisition. Secondly, we integrated Convolutional Block Attention Module (CBAM) into the YOLOv8 model, dynamically enhancing crack feature extraction through both channel and spatial attention mechanisms, thereby improving recognition robustness in complex backgrounds. Lastly, a skeleton extraction algorithm was applied for the secondary processing of the segmented cracks, enabling precise calculations of crack length and average width and outputting the results to a user interface for visualization. The experimental results demonstrate that the system successfully identifies and extracts crack regions, accurately calculates crack dimensions, and enables real-time monitoring through high-speed data transmission to the ground station. Compared to traditional manual inspection methods, the system significantly improves detection efficiency while maintaining high accuracy and reliability. Full article

In response to the challenges of the low accuracy and high misdetection and omission rate of disease detection on feeder roads, an improved Rural-YOLO (SAConv-C2f+C2PSA_CAA+MCSAttention+WIOU) disease detection algorithm is proposed in this paper, which is an enhanced target detection framework based on the YOLOv11 architecture, for the identification of common diseases in the complex feeder road environment. The proposed methodology introduces four key innovations: (1) Switchable Atrous Convolution (SAConv) is introduced into the backbone network to enhance multiscale disease feature extraction under occlusion conditions; (2) Multi-Channel and Spatial Attention (MCSAttention) is constructed in the feature fusion process, and the weight distribution of multiscale diseases is adjusted through adaptive weight redistribution. By adjusting the weight distribution, the model’s sensitivity to subtle disease features is improved. To enhance its ability to discriminate between different disease types, Cross Stage Partial with Parallel Spatial Attention and Channel Adaptive Aggregation (C2PSA_CAA) is constructed at the end of the backbone network. (3) To mitigate category imbalance issues, Weighted Intersection over Union loss (WIoU_loss) is introduced, which helps optimize the bounding box regression process in disease detection and improve the detection of relevant diseases. Based on experimental validation, Rural-YOLO demonstrated superior performance with minimal computational overhead. Only 0.7 M additional parameters is required, and an 8.4% improvement in recall and a 7.8% increase in mAP₅₀ were achieved compared to the initial models. The optimized architecture also reduced the model size by 21%. The test results showed that the proposed model achieved 3.28 M parameters with a computational complexity of 5.0 GFLOPs, meeting the requirements for lightweight deployment scenarios. Cross-validation on multi-scenario public datasets was carried out, and the model’s robustness across diverse road conditions. In the quantitative experiments, the center skeleton method and the maximum internal tangent circle method were used to calculate crack width, and the pixel occupancy ratio method was used to assess the area damage degree of potholes and other diseases. The measurements were converted to actual physical dimensions using a calibrated scale of 0.081:1. Full article

Advancements in image processing and deep learning offer considerable opportunities for automated defect assessment in civil structures. However, these systems cannot work interactively with human inspectors. Mixed reality (MR) can be adopted to address this by involving inspectors in various stages of the assessment process. This paper integrates You Only Look Once (YOLO) v5n and YOLO v5m with the Canny algorithm for real-time concrete crack detection and skeleton extraction with a Microsoft HoloLens 2 MR device. The YOLO v5n demonstrates a superior mean average precision (mAP) 0.5 and speed, while YOLO v5m achieves the highest mAP 0.5 0.95 among the other YOLO v5 structures. The Canny algorithm also outperforms the Sobel and Prewitt edge detectors with the highest F1 score. The developed MR-based system could not only be employed for real-time defect assessment but also be utilized for the automatic recording of the location and other specifications of the cracks for further analysis and future re-inspections. Full article

This study aims to enhance pavement crack detection methods by integrating unmanned aerial vehicles (UAVs) with deep learning techniques. Current methods encounter challenges such as low accuracy, limited efficiency, and constrained application scenarios. We introduce an innovative approach that employs a UAV equipped with a binocular camera for identifying pavement surface cracks. This method is augmented by a binocular ranging algorithm combined with edge detection and skeleton extraction algorithms, enabling the quantification of crack widths without necessitating a preset shooting distance—a notable limitation in existing UAV crack detection applications. We developed an optimized model to enhance detection accuracy, incorporating the YOLOv5s network with an Efficient Channel Attention (ECA) mechanism. This model features a decoupled head structure, replacing the original coupled head structure to optimize detection performance, and utilizes a Generalized Intersection over Union (GIoU) loss function for refined bounding box predictions. Post identification, images within the bounding boxes are segmented by the Unet++ network to accurately quantify cracks. The efficacy of the proposed method was validated on roads in complex environments, achieving a mean Average Precision (mAP) of 86.32% for crack identification and localization with the improved model. This represents a 5.30% increase in the mAP and a 6.25% increase in recall compared to the baseline network. Quantitative results indicate that the measurement error margin for crack widths was 10%, fulfilling the practical requirements for pavement crack quantification. Full article

Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang–Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang–Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks. Full article

This paper proposes a road crack detection algorithm based on an improved SparseInst network, called the SparseInst-CDSM algorithm, aimed at solving the problems of low recognition accuracy and poor real-time detection of existing algorithms. The algorithm introduces the CBAM module, DCNv2 convolution, SPM strip pooling module, MPM mixed pooling module, etc., effectively improving the integrity and accuracy of crack recognition. At the same time, the central axis skeleton of the crack is extracted using the central axis method, and the length and maximum width of the crack are calculated. In the experimental comparison under the self-built crack dataset, SparseInst-CDSM has an accuracy of 93.66%, a precision of 67.35%, a recall of 66.72%, and an IoU of 84.74%, all higher than mainstream segmentation models such as Mask-RCNN and SOLO that were compared, reflecting the superiority of the algorithm proposed in this paper. The comparison results of actual measurements show that the algorithm error is within 10%, indicating that it has high effectiveness and practicality. Full article

Crack detection on bridges is an important part of assessing whether a bridge is safe for service. The methods using manual inspection and bridge-inspection vehicles have disadvantages, such as low efficiency and affecting road traffic. We have conducted an in-depth study of bridge-crack detection methods and have proposed a bridge crack identification algorithm for Unet, called the CBAM-Unet algorithm. CBAM (Convolutional Block Attention Module) is a lightweight convolutional attention module that combines a channel attention module (CAM) and a spatial attention module (SAM), which use an attention mechanism on a channel and spatially, respectively. CBAM takes into account the characteristics of bridge cracks. When the attention mechanism is used, the ability to express shallow feature information is enhanced, making the identified cracks more complete and accurate. Experimental results show that the algorithm can achieve an accuracy of 92.66% for crack identification. We used Gaussian fuzzy, Otsu and medial skeletonization algorithms to realise the post-processing of an image and obtain a medial skeleton map. A crack feature measurement algorithm based on the skeletonised image is proposed, which completes the measurement of the maximum width and length of the crack with errors of 1–6% and 1–8%, respectively, meeting the detection standard. The bridge crack feature extraction algorithm we present, CBAM-Unet, can effectively complete the crack-identification task, and the obtained image segmentation accuracy and parameter calculation meet the standards and requirements. This method greatly improves detection efficiency and accuracy, reduces detection costs and improves detection efficiency. Full article

In the process of road pavement health and safety assessment, crack detection plays a pivotal role in a preventive maintenance strategy. Recently, Convolutional Neural Networks (CNNs) have been applied to automatically identify the cracks on concrete pavements. The effectiveness of a CNN-based road crack detection and measurement method depends on several factors, including the image segmentation of cracks with complex topology, the inference of noises with similar texture to the distress, and the sensitivity to thin cracks. The presence of shadows, strong light reflections, and road markings can also severely affect the accuracy in detection and measurement. In this study, a review of the state-of-the-art CNN methods for crack identification is presented, paying attention to existing limitations. Then, a novel deep residual convolutional neural network (Parallel ResNet) is proposed with the aim of creating a high-performance pavement crack detection and measurement system. The challenge and special feature of Parallel ResNet is to remove the noise inference, identifying even thin and complex cracks correctly. The performance of Parallel ResNet has been investigated on two publicly available datasets (CrackTree200 and CFD), comparing it with that of competing methods suggested in the literature. Parallel ResNet reached the maximum scores in Precision (94.27%), Recall (92.52%), and F1 (93.08%) using the CrackTree200 dataset. Similarly, for the CFD dataset the novel method achieved high values in Precision (96.21%), Recall (95.12%), and F1 (95.63%). Based on the crack detection and image recognition results, mathematical morphology was then used to further minimize noise and accurately segment the road diseases, obtaining the outer contours of the connected domain in crack images. Therefore, crack skeletons have been extracted to measure the distress length, width, and area on images of rigid pavements. The experimental results show that Parallel ResNet can effectively minimize noise to obtain the geometry of cracks. The results of crack characteristic measurements are accurate and Parallel ResNet can be assumed as a reliable method in pavement crack image analysis, in order to plan the best road maintenance strategy. Full article

This paper presents a novel algorithm for detecting pavement cracks from mobile laser scanning (MLS) data. The algorithm losslessly transforms MLS data into a regular grid structure to adopt the proven image-based methods of crack extraction. To address the problem of lacking topology, this study assigns a two-dimensional index for each laser point depending on its scanning angle or acquisition time. Next, crack candidates are identified by integrating the differential intensity and height changes from their neighbors. Then, morphology filtering, a thinning algorithm, and the Freeman codes serve for the extraction of the edge and skeleton of the crack curves. Further than the other studies, this work quantitatively evaluates crack shape parameters: crack direction, width, length, and area, from the extracted crack points. The F1 scores of the quantity of the transverse, longitudinal, and oblique cracks correctly extracted from the test data reached 96.55%, 87.09%, and 81.48%, respectively. In addition, the average accuracy of the crack width and length exceeded 0.812 and 0.897. Experimental results demonstrate that the proposed approach is robust for detecting pavement cracks in a complex road surface status. The proposed method is also promising in serving the extraction of other on-road objects. Full article

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement. Full article