Intelligent Graph Convolutional Neural Network for Road Crack Detection

Djenouri, Youcef; Belhadi, Asma; Houssein, Essam H.; Srivastava, Gautam; Lin, Jerry Chun‐Wei

doi:10.1109/tits.2022.3215538

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…Nguyen et al [16] proposed a two-stage convolutional neural network, where the first stage is used to denoise and isolate potential cracks to a region, and the second stage learns to detect the background of cracks in the region. Hac et al [17] proposed the use of Fast R-CNN for crack detection, and Djenouri et al [18] proposed a crack detection scheme using the scale invariant feature transformation (SIFT) algorithm to analyze the correlation between features to generate a series of graphs that are trained using a graph convolutional neural network and supervised using a super optimization algorithm. Jiang et al [19] proposed an extended version of the U-Net framework, named MSK-UNet, for crack detection.…”

Section: Highway Crack Detectionmentioning

confidence: 99%

Highway Crack Detection and Classification Using UAV Remote Sensing Images Based on CrackNet and CrackClassification

Zhao,

Zhou,

Wang

et al. 2023

Applied Sciences

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Cracks are a common type of road distress. However, the traditional manual and vehicle-borne methods of detecting road cracks are inefficient, with a high rate of missed inspections. The development of unmanned aerial vehicles (UAVs) and deep learning has led to their use in crack detection and classification becoming an increasingly popular topic. In this paper, an aerial drone is used to efficiently and safely collect road data. However, this also brings many challenges. For example, flying too high or too fast may produce poor quality images, with unclear cracks that may be ignored or misjudged as other features and increased environmental noise that may make it difficult to distinguish between cracks and other noise features. To address the above challenges, this paper proposes the CrackNet model and CrackClassification algorithm. The CrackNet network is an encoder–decoder architecture. Low- and high-level semantic information are combined through the skip feature fusion layers between the encoder and decoder to enhance the model’s expression and ability to recover image details. Additionally, the MHDC module at the bottom of the network can significantly increase the receptive field without reducing the feature map resolution. The MHSA module can simultaneously capture features from multiple subspaces. The average precision (AP) scores of the CrackNet network on three datasets, namely UAVRoadCrack, CRKWH100, and CrackLS315, were 0.665, 0.942, and 0.895, respectively. In addition, values of the other two evaluation metrics, ODS and OIS, were the highest among the compared methods. Meanwhile, the proposed CrackClassification algorithm in this paper achieves 85% classification accuracy for transverse and longitudinal cracks and 78% classification accuracy for block cracks and reticulated cracks. Overall, the CrackNet algorithm provides a new baseline model for crack detection in UAV remote sensing image scenes. The CrackClassification algorithm provides a new approach for batch classification of highway cracks. The detection and classification algorithm proposed in this paper were applied to 108 km of road sections.

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Section: Highway Crack Detectionmentioning

confidence: 99%

Highway Crack Detection and Classification Using UAV Remote Sensing Images Based on CrackNet and CrackClassification

Zhao,

Zhou,

Wang

et al. 2023

Applied Sciences

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“…Djenouri et al [ 25 ] proposed a method to detect road cracks using a graph convolutional neural network (GCNN). They computed the visual features of roads using scale invariant feature transformation (SIFT) and then analyzed a correlation between SIFT features of similar images.…”

Section: Related Workmentioning

confidence: 99%

Predictive Maintenance of Norwegian Road Network Using Deep Learning Models

Hassan

Steinnes

Gustafsson

et al. 2023

Sensors

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Industry 4.0 has revolutionized the use of physical and digital systems while playing a vital role in the digitalization of maintenance plans for physical assets in an optimal way. Road network conditions and timely maintenance plans are essential in the predictive maintenance (PdM) of a road. We developed a PdM-based approach that uses pre-trained deep learning models to recognize and detect the road crack types effectively and efficiently. We, in this work, explore the use of deep neural networks to classify roads based on the amount of deterioration. This is done by training the network to identify various types of cracks, corrugation, upheaval, potholes, and other types of road damage. Based on the amount and severity of the damage, we can determine the degradation percentage and have a PdM framework where we can identify the intensity of damage occurrence and, thus, prioritize the maintenance decisions. The inspection authorities and stakeholders can make maintenance decisions for certain types of damages using our deep learning-based road predictive maintenance framework. We evaluated our approach using precision, recall, F1-score, intersection-over-union, structural similarity index, and mean average precision measures, and found that our proposed framework achieved significant performance.

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“…The appearance of asphalt road surface disease not only affects the maintenance quality, but also endangers the safety of driving, so it is particularly important to detect and manage the road health condition [1][2][3]. The traditional manual detection method has the problems of low efficiency [4,5] and low accuracy [6][7][8]. Therefore, rapid realization of nondestructive detection of pavement damage is urgent to be solved [9].…”

Section: Introductionmentioning

confidence: 99%

Road detection and three-dimensional visualization management using deep learning and BIM integration

Liu,

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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Asphalt pavement detection is a problem characterized by low efficiency, high manual input, and high cost. A 3D visualization management platform for road infrastructures based on artificial intelligence and information technology is presented in this study. Initially, images of cracks and potholes were processed digitally and segmented using image processing methods. A pavement condition index (PCI) is calculated by combining pavement distress identification with cracks and potholes. After that, ground penetrating radar (GPR) was used to detect the road structures, and then Canny edge algorithm was performed to process the original GPR images. Cracks, looseness, and poor interlayering were identified with the object detection model and data enhancement methods. Inverse time migration (3D) imaging of the internal diseases was performed in three dimensions (3D). Based on the results of the object detection model, F1 score and mAP were both above 80% for road distress detection. As a final step, a BIM model of asphalt pavement was generated using Revit software, and 3D asphalt pavement model rendering and quality details were displayed in Web-BIM browser, suggesting that this method can be used to maintain highways in a fast, intelligent, and scientific manner.

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Intelligent Graph Convolutional Neural Network for Road Crack Detection

Cited by 10 publications

References 38 publications

Highway Crack Detection and Classification Using UAV Remote Sensing Images Based on CrackNet and CrackClassification

Highway Crack Detection and Classification Using UAV Remote Sensing Images Based on CrackNet and CrackClassification

Predictive Maintenance of Norwegian Road Network Using Deep Learning Models

Road detection and three-dimensional visualization management using deep learning and BIM integration

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