Affiliation:
1. Technology Research and Development Department, Guangzhou Municipal Engineering Testing Co., Ltd., Guangzhou, The People's Republic of China
Abstract
Crack detection in pavements is a critical task for infrastructure maintenance, but it often requires extensive manual labeling of training samples, which is both time-consuming and labor-intensive. To address this challenge, this paper proposes a semi-supervised learning approach based on a DenseNet classification model to detect pavement cracks more efficiently. The primary objective is to leverage a small set of labeled samples to improve the model's performance by incorporating a large number of unlabeled samples through semi-supervised learning. This method enhances the DenseNet model's ability to generalize by iteratively learning from new unlabeled datasets. As a result, the proposed approach not only reduces the need for extensive manual labeling but also mitigates issues related to label inconsistency and errors in the original labels. The experimental results demonstrate that the semi-supervised DenseNet model achieves a prediction precision of 96.77% and a recall of 94.17%, with an F1 score of 95.45% and an Intersectidn over Union (IoU) of 91.30%. These metrics highlight the model's high accuracy and effectiveness in crack detection. The proposed method not only improves label quality and model performance but also offers practical value for engineering applications in the field of pavement maintenance, making it a valuable tool for infrastructure management.
Reference38 articles.
1. Zhang, J.; Yang, X.; Wang, W.; Brilakis, I.; Davletshina, D.; Wang, H.; Cao, M. Segment-to-track for pavement crack with light-weight neural network on unmanned wheeled robot. Automation in Construction 2024, 161, 105346, https://doi.org/10.1016/j.autcon.2024.105346
2. Yu, Z.; Shen, Y.; Zhang, Y.; Xiang, Y. Automatic crack detection and 3D reconstruction of structural appearance using underwater wall-climbing robot. Automation in Construction 2024, 160, 105322, https://doi.org/10.1016/j.autcon.2024.105322
3. Kirschke, K. R.; Velinsky, S. A. Histogram-Based Approach for Automated Pavement-Crack Sensing. Journal of Transportation Engineering 1992, 118, 700-710, https://doi.org/10.1061/(ASCE)0733-947X(1992)118:5(700)
4. Bhutani, K. R.; Battou, A. An application of fuzzy relations to image enhancement. Pattern Recognition Letters 1995, 16, 901-909, https://doi.org/10.1016/0167-8655(95)00035-F
5. Oliveira, H.; Correia, P. L. Automatic road crack segmentation using entropy and image dynamic thresholding. In Proceedings of 2009 17th European Signal Processing Conference.