Automated classification and detection of multiple pavement distress images based on deep learning

Li, Deru; Duan, Zhongdong; Hu, Xiaoyang; Zhang, Dongchang; Zhang, Yiying

doi:10.1016/j.jtte.2021.04.008

Cited by 13 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, deep learning methods are the primary approach for achieving classification and identification tasks related to pavement cracks, enabling the identification of whether the image contains cracks and crack types such as transverse, longitudinal, etc., by enhancing classical classification and recognition networks like AlexNet [21][22][23], VGG [24][25], and ResNet [26][27][28][29]. However, the utilization of deep learning methods for crack classification and recognition presents significant challenges: (1) The creation of training sets and model training demands substantial time and often requires high-end hardware systems.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Tao,

2024

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Wang,

Tao,

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Surface texture image classification of carbon/phenolic composites in extreme environments using deep learning

Shang,

Yang,

et al. 2024

Polymer Composites

View full text Add to dashboard Cite

The classification of ablation images holds significant practical value in thermal protection structures, as it enables the assessment of heat and corrosion resistance of composites. This paper proposes an image‐based deep learning framework to identify the surface texture of carbon/phenolic composites ablative images. First, ablation experiments and collection of surface texture images of carbon/phenolic composites under different thermal environments were conducted in an electric arc wind tunnel. Then, a deep learning model based on a convolutional neural network (CNN) is developed for ablative image classification. The pre‐trained network is ultimately employed as the input for transfer learning. The network's feature extraction layer is trained using the ImageNet dataset, while the global average pooling addresses specific classification tasks. The test results demonstrate that the proposed method effectively classifies the relatively small surface texture dataset, enhances the classification performance of ablative surface texture with an accuracy of up to 97.6%, and exhibits robustness and generalization capabilities.Highlights The paper proposes a new deep learning classification method for ablative images. A model highly sensitive to small and weak features is built. Transfer learning and data enhancement techniques are introduced into classification.

show abstract

Road Distress Detection and Classification: Harnessing the Synergy of Deep Learning and Transfer Learning Approaches

Khlifati,

Baba

2023

Springer Proceedings in Earth and Environmental Sciences

View full text Add to dashboard Cite

Automated classification and detection of multiple pavement distress images based on deep learning

Cited by 13 publications

References 28 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

Surface texture image classification of carbon/phenolic composites in extreme environments using deep learning

Road Distress Detection and Classification: Harnessing the Synergy of Deep Learning and Transfer Learning Approaches

Contact Info

Product

Resources

About