Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

Li, Zhongbo; Yin, Chao; Zhang, Xixuan

doi:10.3390/s23229161

Cited by 5 publications

(7 citation statements)

References 48 publications

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“…c is the parameter controlling the rate of decay, determined by 𝛿, 𝑐 = −16log[0.1(𝛿 − 1)] π 2 ⁄ . The voting field intensity S of point O at any given point P is determined by (5).…”

Section: Geometric Representationmentioning

confidence: 99%

“…Many studies focus on enhancing the classical Otsu threshold method, K-means clustering, and other algorithms to determine the optimal gray value segmentation threshold. Li et al [5] employed histogram equalization and linear gray value transformation to accentuate crack features, eliminate noise, and applied the Zhang-Suen refinement algorithm and connectivity domain threshold method for crack skeleton extraction and skeleton burrs removal. Vivekananthan et al [6] utilized the Sobel filter for crack edge detection, combining max-min gray level discrimination with the Otsu method for improved segmentation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Pavement Crack Classification and Recognition Algorithm Combined With Tensor Voting and RANSAC

Wang,

Tao,

2024

IEEE Access

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The existing methods for pavement crack classification and identification solely offer information about the crack type, neglecting size and direction details, which are essential for guiding repair efforts and forming the engineer digital information data. In response to the challenges posed by insufficient crack information, prolonged training time and intricate parameter adjustment inherent in employing deep learning algorithms for pavement crack classification and recognition, we propose an integrated approach combining tensor voting with the random sample consensus for pavement crack classification and recognition. The method involves pre-processing road images using gray value transformation and the K-Means clustering algorithm. Subsequently, the tensor voting algorithm is applied to enhance the linear features, resulting in the generation of linear saliency maps of cracks along with crack junction information. Furthermore, a nonmaximum suppression method and the RANSAC algorithm are employed to refine and fit the crack skeleton curves respectively, accomplishing the crack classification and recognition. The outcomes demonstrate that the proposed integrated approach in the crack skeleton segmentation algorithm yields an average F1-score of 0.7879, outperforming traditional non-maximum suppression methods. The accuracy of crack classification and recognition reaches 96%, outperforming other crack classification and recognition algorithms grounded in digital image processing methods. Compared with the neural networks employed for classification and recognition, the proposed algorithm is able to capture direction and size details of cracks, which can provide guidance for intelligent crack repair. This additional information can offer valuable guidance for intelligent crack repair processes.INDEX TERMS Crack classification and recognition, non-maximum suppression, random sample consensus (RANSAC), tensor voting.

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Section: Geometric Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pavement Crack Classification and Recognition Algorithm Combined With Tensor Voting and RANSAC

Wang,

Tao,

2024

IEEE Access

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“…Nevertheless, DL models often consume large computational resources, resulting in slower processing times for classification systems. To address that issue, classical image processing algorithms with lower computations have been employed for mango segmentation, such as the approach of utilizing a fixed threshold to segment objects from grayscale images, as described in the study in [14]. However, that method lacks adaptability when the color of the mangoes has changed.…”

Section: Mango Segmentationmentioning

confidence: 99%

“…standard deviation of one, thereby eliminating differences arising from varying scales among the features. The equation for Standard Scaler is shown as follows (14).…”

Section: Data Preprocessingmentioning

confidence: 99%

Development of a Mango-Grading and -Sorting System Based on External Features, Using Machine Learning Algorithms

Tai,

Lin,

Trieu

et al. 2024

Agronomy

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Nowadays, many image processing and machine learning (ML) methods are used in mango-quality classification systems. Existing algorithms perform independently to capture the relationships between features in the dataset. Nevertheless, each method has its advantages and disadvantages. As a result, this study presents an ensemble-learning framework that combines the strengths of multiple ML algorithms to make predictions. Initially, different image processing algorithms are used to extract external mango features. Next, the dataset is constructed by combining those features with weight values from sensor signals. Following that, different ML algorithms are evaluated on the dataset to determine which ones are robust. Subsequently, various ensemble-learning approaches are deployed, such as bagging, boosting, and stacking. Finally, those models are evaluated and compared, to decide which model is suited for this study’s dataset. In the experimental part, the assessment of errors demonstrates the usefulness of image processing algorithms. Furthermore, evaluation of the training models revealed that the stacking model, which integrates several methods in both the base learner and meta-learner, produced the highest results in precision, recall, F1-score, and accuracy, with values of 0.9855, 0.9901, 0.9876, and 0.9863, respectively. These experimental results confirm the robustness of the proposed methodologies for classifying mango quality.

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“…Test results showed that the average detection error of the model was only 6.521 pixels, demonstrating its precision in pavement defect localization. However, although two-stage target detection models exhibit higher accuracy in pavement distress detection tasks, the direct application in engineering detection tasks is limited due to their large model parameters, high computational complexity, and slower recognition speed compared to one-stage detection models 22 .…”

Section: Introductionmentioning

confidence: 99%

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning

Zhang,

Bei,

Ling

et al. 2024

Preprint

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Accurate detection of asphalt pavement distress is crucial for road maintenance and traffic safety. However, traditional convolutional neural networks usually struggle with this task due to the varied distress patterns and complex backgrounds in the images. To enhance the accuracy of asphalt pavement distress identification across various scenarios, this paper introduces an improved model named SMG-YOLOv8, based on the YOLOv8s framework. This model integrates the space-to-depth module and the multi-scale convolutional attention mechanism, while optimizing the backbone's C2f structure with a more efficient G-GhostC2f structure. Experimental results demonstrate that SMG-YOLOv8 outperforms the YOLOv8s baseline model, achieving Pmacro and mAP@50 scores of 81.1% and 79.4% respectively, marking an increase of 8.2% and 12.5% over the baseline. Furthermore, SMG-YOLOv8 exhibits clear advantages in identifying various types of pavement distresses, including longitudinal cracks, transverse cracks, mesh cracks, and potholes, when compared to YOLOv5n, YOLOv5s, YOLOv6s, and YOLOv8n models. This enhancement optimizes the network structure, reducing the number of parameters while maintaining excellent detection performance. In real-world scenarios, the SMG-YOLOv8 model has demonstrated strong generalization capability and practical utility, providing crucial technical support for intelligent pavement distress detection.

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Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

Cited by 5 publications

References 48 publications

Pavement Crack Classification and Recognition Algorithm Combined With Tensor Voting and RANSAC

Pavement Crack Classification and Recognition Algorithm Combined With Tensor Voting and RANSAC

Development of a Mango-Grading and -Sorting System Based on External Features, Using Machine Learning Algorithms

Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning

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