Road cracks pose a significant challenge to pavement maintenance by affecting structural integrity, jeopardizing traffic safety, and reducing driving comfort. With the limitations of manual inspection methods, such as being labor intensive, time consuming, and prone to human error, automated detection techniques have emerged as efficient and scalable alternatives. This study presents a detailed comparative analysis of state-of-the-art crack detection models, i.e., YOLOv7, VGG-19, ResNet-50, Naive Bayes, and deep convolutional neural networks, evaluating their performance on diverse and complex pavement image datasets. To ensure fairness and consistency, all models were trained and tested under identical conditions. ResNet-50 demonstrated superior performance, achieving the highest accuracy, that is, 99.8% in detecting and segmenting cracks in a variety of pavement scenarios. Its ability to balance precision and robustness makes it a leading solution for automated crack detection.
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