Article
IMPROVED CRACK DETECTION THROUGH STYLEGANAUGMENTED DEEPLABV3 WITH RESNET50 BACKBONE
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.
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