DynaEdge-Net: Dynamic Feature Gating and Edge Enhancement for Precise Road Crack Segmentation

Highway crack segmentation is a critical task for highway infrastructure monitoring and maintenance. While imagery from unmanned aerial vehicles (UAVs) is applied to the task of highway crack segmentation, it has great prospects in terms of speed and range. However, it is difficult to accurately identify road cracks from UAV remote sensing images, because the cracks are very narrow and small, often containing only a few pixels. To improve the segmentation of road cracks in UAV images, this study proposed an improved identification technique based on the U-Net architecture enhanced with a convolutional block attention module, an improved encoder, and the strategy of fusing long and short skip connections. A public road crack dataset was relabelled for network training and a UAV remote sensing road crack dataset containing 1157 images was used to verify the generalization ability of the enhanced network model. Results showed that the proposed method could effectively predict highway cracks in UAV images, with mean intersection over union (mIoU) of 77.47% and crack accuracy of 68.38%, which was better than the traditional U-Net model and some traditional semantic segmentation models. The proposed network is trained quickly by public dataset and can predict the road cracks on the new UAV images with high crack accuracy. This study provides an effective solution for the need to quickly grasp the damage status of roads over a wide area in the case of earthquake and other natural disasters. The highway crack segmentation benchmark dataset has been open sourced at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhhongsh/UAV-Benchmark-Dataset-for-Highway-Crack-Segmentation</uri> .

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.

Image-based concrete crack detection in tunnels using deep fully convolutional networks

Road cracks are one of the external manifestations of safety hazards in transportation. At present, the detection and segmentation of road cracks is still an intensively researched issue. With the development of image segmentation technology of the convolutional neural network, the identification of road cracks has also ushered in new opportunities. However, the traditional road crack segmentation method has these three problems: 1. It is susceptible to the influence of complex background noise information. 2. Road cracks usually appear in irregular shapes, which increases the difficulty of model segmentation. 3. The cracks appear discontinuous in the segmentation results. Aiming at these problems, a network segmentation model of HC-Unet++ road crack detection is proposed in this paper. In this network model, a deep parallel feature fusion module is first proposed, one which can effectively detect various irregular shape cracks. Secondly, the SEnet attention mechanism is used to eliminate complex backgrounds to correctly extract crack information. Finally, the Blurpool pooling operation is used to replace the original maximum pooling in order to solve the crack discontinuity of the segmentation results. Through the comparison with some advanced network models, it is found that the HC-Unet++ network model is more precise for the segmentation of road cracks. The experimental results show that the method proposed in this paper has achieved 76.32% mIOU, 82.39% mPA, 85.51% mPrecision, 70.26% dice and Hd95 of 5.05 on the self-made 1040 road crack dataset. Compared with the advanced network model, the HC-Unet++ network model has stronger generalization ability and higher segmentation accuracy, which is more suitable for the segmentation detection of road cracks. Therefore, the HC-Unet++ network model proposed in this paper plays an important role in road maintenance and traffic safety.

Deep learning-based methods, especially convolutional neural networks, have been developed to automatically process the images of concrete surfaces for crack identification tasks. Although deep learning-based methods claim very high accuracy, they often ignore the complexity of the image collection process. Real-world images are often impacted by complex illumination conditions, shadows, the randomness of crack shapes and sizes, blemishes, and concrete spall. Published literature and available shadow databases are oriented towards images taken in laboratory conditions. In this paper, we explore the complexity of image classification for concrete crack detection in the presence of demanding illumination conditions. Challenges associated with the application of deep learning-based methods for detecting concrete cracks in the presence of shadows are elaborated on in this paper. Novel shadow augmentation techniques are developed to increase the accuracy of automatic detection of concrete cracks.

Crack segmentation is essential for structural health monitoring and infrastructure maintenance, playing a crucial role in early damage detection and safety risk reduction. Traditional methods, including digital image processing techniques have limitations in complex environments. Deep learning-based methods have shown potential, but still face challenges, such as poor generalization with limited samples, insufficient extraction of fine-grained features, feature loss during upsampling, and inadequate capture of crack edge details. This study proposes SECrackSeg, a high-accuracy crack segmentation network that integrates an improved UNet architecture, Segment Anything Model 2 (SAM2), MI-Upsampling, and an Edge-Aware Attention mechanism. The key innovations include: (1) using a SAM2 S-Adapter with a frozen backbone to enhance generalization in low-data scenarios; (2) employing a Multi-Scale Dilated Convolution (MSDC) module to promote multi-scale feature fusion; (3) introducing MI-Upsampling to reduce feature loss during upsampling; and (4) implementing an Edge-Aware Attention mechanism to improve crack edge segmentation precision. Additionally, a custom loss function incorporating weighted binary cross-entropy and weighted IoU loss is utilized to emphasize challenging pixels. This function also applies Multi-Granularity Supervision by optimizing segmentation outputs at three different resolution levels, ensuring better feature consistency and improved model robustness across varying image scales. Experimental results show that SECrackSeg achieves higher precision, recall, F1-score, and mIoU scores on the CFD, Crack500, and DeepCrack datasets compared to state-of-the-art models, demonstrating its excellent performance in fine-grained feature recognition, edge segmentation, and robustness.

In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset containing complex environmental noise is produced. Different shooting distances, angles, and lighting conditions are considered. Validated through the same dataset and compared with You Only Look at CoefficienTs ++ (YOLACT++) and DeepLabv3+, the proposed method shows higher accuracy for crack segmentation under complex backgrounds. Specifically, the crack damage detection based on the YOLOv5 method achieves a mean average precision of 91%; the modified Res-UNet achieves 87% intersection over union (IoU) when segmenting crack pixels, 6.7% higher than the original Res-UNet; and the developed crack surface feature algorithm has an accuracy of 95% in identifying the crack length and a root mean square error of 2.1 pixels in identifying the crack width, with the accuracy being 3% higher in length measurement than that of the traditional method.

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.

Rapid and effective identification and extraction of road cracks has always been a major difficulty in road detection and maintenance. This paper applies the Deeplabv3 + model to road crack extraction, and proposes a new joint identification method for road cracks based on open network data and open source convolutional neural network. Based on the semantic segmentation theory and Deeplabv3+ neural network, this method uses baidu street view map as the training data set. By adjusting the proportion weight of road cracks and background, the training network model can quickly and accurately identify road cracks. Experimental results show that this method has achieved a good effect on crack segmentation: Mean Intersection over Union(MIOU) of this method is more than 70%, and the processing speed is 1 second/sheet, which is better than FCN algorithm.The results of road crack information extraction are similar to those of manual interpretation, which indicates that this method is feasible for quality evaluation of municipal roads.

Computer vision framework for crack detection of civil infrastructure—A review

Vision based nighttime pavement cracks pixel level detection by integrating infrared visible fusion and deep learning

Crack is a common disease of pavement, which will lead to more serious problems if it is not found and maintained in time. This means that it is very important to accurately extract and measure the damage information of pavement cracks. Compared with the traditional methods, the automatic detection and segmentation of pavement cracks using visual elements are more effective which has become a focused area. Although extensive researches has used deep learning methods in pavement crack detection, these methods only involve the single task of detection or segmentation, and few research optimises and combines them. In addition, the accuracy and inference speed of pavement crack detection and segmentation algorithm is also worthy of further research. To solve these limitations, this research proposes a new method of two-stage pavement crack detection and segmentation based on deep learning. The proposed method combines pavement crack detection and segmentation. In the first stage, the optimised YOLOv4 is used as the pavement crack damage detection algorithm to detect pavement cracks under various complex backgrounds. In the second stage, the cracks detected in the first stage are segmented, the detection accuracy is specific to the damage pixels. To further optimise the performance of the detection and segmentation algorithm, a new deeplabv3+ pavement crack segmentation method based on the Ghost module and CBAM attention mechanism is proposed. Compared with the original network, the proposed two-stage pavement damage detection and segmentation method improve the detection accuracy by 2.23 % and 7.47 % , respectively. The network inference speed is improved by 35.3 % and 50.3 % , respectively. Compared with the existing single-stage pavement damage detection or segmentation methods, the proposed method has the advantages of fast inference speed and high detection accuracy.

Automatic road crack detection is a prominent challenging task, in view of that, a novel approach is proposed using multi-tasking Faster-RCNN to detect and classify road cracks. In this present study, we have collected the road images (a dataset of 19300 images) from the Outer Ring Road of Chennai, Tamil Nadu, India. The collected road images were pre-processed using various conventional image processing techniques to identify the ground-truth label of the bounding boxes for the cracks. We present a novel multi-tasking Faster-RCNN based approach using the Global Average Pooling(GAP) and Region of Interest (RoI) Align techniques to detect the road cracks. The RoI Align is used to avoid quantizing the stride. So that the information loss can be minimized and the bi-linear interpolation can be used to map the proposal to the input image. The resulting features from RoI Align are given as input to the GAP layer which drastically reduces the multi-dimension features into a single feature map. The output of the GAP layer is given to the fully connected layer for classification (softmax) and also to a regression model for predicting the crack location using a bounding box. F1-measure, precision, and recall were used to evaluate the results of classification and detection. The proposed model achieves the accuracy-97.97%, precision-99.12%, and recall-97.25% for classification using the MIT-CHN-ORR dataset. The experimental results show, that the proposed approach outperforms the other state-of-the-art methods.

Automatic detection and segmentation of airport pavement cracks has always been the focus of attention of the field management department. Due to the different background, shape, color and size of cracks, traditional methods cannot accurately extract crack information from the road surface image with complex background. Therefore, this paper proposes a deep learning-based image detection method for cracks pixel-level segmentation. The proposed network is an encoder-decoder network structure. The encoder uses VGG19 as the backbone network to extract crack features. A spatial pyramid pooling module is introduced between the encoder and decoder to obtain the global crack information. The hole convolution and multi-loss supervision function are introduced to obtain a larger receptive field and improve the segmentation effect of small cracks. This model can be used for efficient multi-scale feature extraction, aggregation and resolution reconstruction, thereby greatly enhancing the fracture segmentation capability of the network. Compared with traditional image processing and other deep learning-based crack segmentation methods, this algorithm has higher accuracy and generalization ability on complex background airport pavements, making the automatic detection and monitoring of airport pavement more efficient.

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.