Abstract

Pavement crack detection needs to be done to identify and assess cracks in road surfaces. Detecting the crack and its measurement by manual methods is extremely time-consuming and requires a lot of manpower. This process is crucial for maintaining road safety and infrastructure integrity. By detecting cracks early, authorities can prioritize repairs and prevent further damage, ultimately extending the lifespan of roads and reducing maintenance costs. Additionally, crack detection helps improve driving conditions and safety for motorists by enabling timely repairs to be made. Overall, pavement crack detection plays a vital role in ensuring the durability, safety, and efficiency of road networks. Some factors, such as non-uniform intensity, complexity, and irregular patterns of cracks, complicate the process, and the accuracy of the results may be affected. The aim of this study is to develop a practical crack segmentation method for real-time maintenance. In this paper, two models are proposed based on U-Net architecture and feature pyramidal network (FPN) architecture. To verify the superiority and generalizability of the proposed method, two publicly available CRACK500 and CFD datasets are used. Metrics such as AIU (Average Intersection over Union) and ODS (Overall Dice Similarity) measure are used to evaluate the performance. These metrics indicate that the proposed method effectively segments cracks in pavement images, demonstrating its potential for use in real-world applications.

Authors

G. Prema, S. Arivazhagan, R. Shriram, R. Sri Venkadesh
Mepco Schlenk Engineering College, India

Keywords

Deep Learning, U-Net, FPN, Crack Segmentation