Image-Based Classification of Concrete Carbonation Using YOLO Models

Detecting the presence of carbonation is critical for monitoring structural safety and durability. Identifying the presence of carbonation reveals the risk of chemical changes within the concrete and the potential for reinforcement corrosion. This detection allows for a reliable and prioritized assessment of the structure's current condition. Therefore, checking for the presence or absence of carbonation is a critical indicator in determining structural safety and maintenance priorities. This study explicitly addresses a critical gap in the literature, where existing carbonation research predominantly focuses on regression-based estimation of carbonation depth, while the problem of direct visual classification of carbonation presence for rapid decision-making currently remains underexplored. In this context, the study aims to fill this research gap through developing a robust and field-applicable deep learning-based classification framework for the automated detection of carbonation presence on concrete surfaces using images, while systematically comparing the performance of different YOLO architectures and assessing the suitability of a previously unused dataset (ConcreteCARB) for carbonation classification tasks. In this context, YOLOv8m, YOLOv11m, YOLOv12m, and YOLOv26m were compared for concrete carbonation classification, aiming to find the most suitable model. The results show that YOLOv8m and YOLOv11m achieve perfect accuracy (Accuracy = 0.9981, Precision = 1, Recall = 0.9964, Specificity = 1, AUC-ROC = 1). In inference efficiency analyses, the YOLOv11m model was identified as the fastest model with the lowest latency and highest FPS. While YOLOv8m and YOLOv26m offered balanced speed-performance results, YOLOv12m showed a relatively lower processing speed. The findings indicate that YOLOv11m is the most suitable option for real-time applications.