Year: 2025
Type: article
Abstract: The maintenance of structural integrity is paramount for ensuring the safety and longevity of critical infrastructure, such as bridges.Conventional methods for structural crack inspection are often manual, labor-intensive, and susceptible to human error.Recent advancements in deep learning and semantic segmentation offer a potential solution to automate this process.However, a significant obstacle remains: the scarcity of high-quality, annotated datasets required to train robust models.This paper presents a novel, enhanced deep learning approach for structural crack detection that integrates a powerful semantic segmentation architecture with state-of-the-art synthetic data generation.The proposed method utilizes the DeepLabV3 model with a ResNet50 backbone to leverage its robust feature extraction and sophisticated multi-scale contextual understanding.To address the challenge of data scarcity, StyleGAN3 is employed to synthesize a large, diverse, and highly realistic dataset of structural crack images.The integration of this synthetic data with the DeepLabV3+ResNet50 model is shown to significantly improve segmentation performance and model generalization.Experimental results demonstrate that the proposed framework achieves superior accuracy when compared to existing state-of-the-art methods.This study not only advances the field of automated structural crack analysis but also establishes a new paradigm for using synthetic data to overcome a fundamental bottleneck in deep learning for civil infrastructure applications.
Authors: Shameela Shaikh, Ms Rubeena Afsar
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414188236
FWCI:
Cites:
Cited by:
Related to: