Pavement crack detection based on a deep learning approach and visualisation by using GIS

Dipankar, Amit; Suman, Sanjeev Kumar

doi:10.1080/10298436.2023.2173754

Cited by 7 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ResNet50 is replaced as the backbone of the model, owing to its advantages in terms of depth, parameter sharing, expressive feature representation, and advanced architectural design. Numerous studies have indicated that this modification leads to improved performance of the model [ [59] , [60] , [61] ]. Feature Pyramid Network (FPN) is added to effectively integrate low-level, mid-level, and high-level features from the backbone, compensating for the loss of feature information.…”

Section: Discussionmentioning

confidence: 99%

Semantic segmentation for tooth cracks using improved DeepLabv3+ model

Xie,

Lu,

Guo

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Semantic segmentation for tooth cracks using improved DeepLabv3+ model

Xie,

Lu,

Guo

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Despite the progress made in pavement and cracking detection, some research gaps still need to be addressed. For instance, the development of more accurate and efficient feature extraction techniques, as well as the integration of multiple data sources [28], could further enhance the performance and robustness of pavement distress identification models [29]. Additionally, developing real-time asphalt distress identification systems could help identify and address pavement distresses before they become severe, thereby reducing maintenance costs and enhancing road safety [30].…”

Section: Related Workmentioning

confidence: 99%

Automated Pavement Crack Detection Using Deep Feature Selection and Whale Optimization Algorithm

Alshawabkeh,

Wu,

Dong

et al. 2023

CMC

View full text Add to dashboard Cite

Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses. Recent advancements in deep learning (DL) techniques have shown promising results in detecting pavement cracks; however, the selection of relevant features for classification remains challenging. In this study, we propose a new approach for pavement crack detection that integrates deep learning for feature extraction, the whale optimization algorithm (WOA) for feature selection, and random forest (RF) for classification. The performance of the models was evaluated using accuracy, recall, precision, F1 score, and area under the receiver operating characteristic curve (AUC). Our findings reveal that Model 2, which incorporates RF into the ResNet-18 architecture, outperforms baseline Model 1 across all evaluation metrics. Nevertheless, our proposed model, which combines ResNet-18 with both WOA and RF, achieves significantly higher accuracy, recall, precision, and F1 score compared to the other two models. These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications. We applied the proposed approach to a dataset of pavement images, achieving an accuracy of 97.16% and an AUC of 0.984. Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection, offering a promising solution for the automatic identification of pavement cracks. By leveraging this approach, potential safety hazards can be identified more effectively, enabling timely repairs and maintenance measures. Lastly, the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection, providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.

show abstract

“…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

Pavement crack detection based on a deep learning approach and visualisation by using GIS

Cited by 7 publications

References 29 publications

Semantic segmentation for tooth cracks using improved DeepLabv3+ model

Semantic segmentation for tooth cracks using improved DeepLabv3+ model

Automated Pavement Crack Detection Using Deep Feature Selection and Whale Optimization Algorithm

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

Contact Info

Product

Resources

About