Robust surface crack detection with structure line guidance

Zhang, Yongjun; Lu, Yixin; Duan, Yansong; Wei, Dong; Zhu, Xianzhang; Zhang, Bin; Pang, Bohui

doi:10.1016/j.jag.2023.103527

Cited by 1 publication

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Edge detection methods use edge detection operators to identify these edges, such as Sobel, Canny, Prewitt, etc. [5][6][7]. However, the choice of parameters in these algorithms can significantly impact the detection of cracks, and finding the optimal parameters for different images, especially those with complex backgrounds, can be challenging.…”

Section: Pavement Crack Detectionmentioning

confidence: 99%

“…The total diffusion steps (T) are the same as in training procedure. From T to 1, the network predicts the mean and variance of noise in the current timestep to denoise GT t and calculate the estimation GT t−1 according to Equation (7).…”

Section: Algorithm 1 Training Algorithmmentioning

confidence: 99%

“…Methods utilizing visible light images for pavement crack detection offer the advantages of cost-effectiveness, enhanced detection speed, and automation. Image-based crack detection methods mainly include threshold segmentation [4], edge detection [5][6][7], traditional machine learning [8][9][10], and deep learning techniques [11][12][13][14][15][16][17][18]. Deep learning models, particularly convolutional neural networks (CNNs) [19], have exhibited significant promise in the field of crack detection because they can automatically learn image features, leading to highly accurate crack detection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Crack Diffusion Model: An Innovative Diffusion-Based Method for Pavement Crack Detection

Zhang,

Chen,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Pavement crack detection is of significant importance in ensuring road safety and smooth traffic flow. However, pavement cracks come in various shapes and forms which exhibit spatial continuity, and algorithms need to adapt to different types of cracks while preserving their continuity. To address these challenges, an innovative crack detection framework, CrackDiff, based on the generative diffusion model, is proposed. It leverages the learning capabilities of the generative diffusion model for the data distribution and latent spatial relationships of cracks across different sample timesteps and generates more accurate and continuous crack segmentation results. CrackDiff uses crack images as guidance for the diffusion model and employs a multi-task UNet architecture to predict mask and noise simultaneously at each sampling step, enhancing the robustness of generations. Compared to other models, CrackDiff generates more accurate and stable results. Through experiments on the Crack500 and DeepCrack pavement datasets, CrackDiff achieves the best performance (F1 = 0.818 and mIoU = 0.841 on Crack500, and F1 = 0.841 and mIoU = 0.862 on DeepCrack).

show abstract

Section: Pavement Crack Detectionmentioning

confidence: 99%

Section: Algorithm 1 Training Algorithmmentioning

confidence: 99%