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. I. INTRODUCTION I.1 Background and MotivationThe structural integrity of civil infrastructure, particularly bridges, is crucial for public safety and sustained functionality. 1 The presence of cracks, if left undetected and unaddressed, can lead to severe structural degradation and, in the most extreme cases, catastrophic failure. 1 Historically, the inspection of these structures has been a meticulous and time-consuming process that relies heavily on visual analysis and manual, physical examination. 1This approach is inherently subjective, prone to human fatigue, and often impracticable for large-scale infrastructure networks, leading to a high potential for errors and delayed maintenance. 1 In recent years, the fields of computer vision and deep learning have provided an unprecedented opportunity to automate and significantly enhance the accuracy and efficiency of structural health