Conventional visual inspections of concrete structures are hazardous, time-consuming, and prone to subjectivity, which has accelerated the adoption of automated image-based techniques for structural health monitoring. Deep learning methods, particularly convolutional neural networks (CNNs), offer significant potential for crack detection, yet their accuracy is often compromised by image noise arising from environmental conditions, sensor artefacts, and preprocessing. This study systematically evaluates the integra…