CGSW-YOLO Enhanced YOLO Architecture for Automated Crack Detection in Concrete Structures

Cracks in concrete structures are key indicators for structural health diagnosis, and the demand for automated detection is gradually increasing. Although various non-destructive testing (NDT) methods and concrete defect detection software have been widely applied, their detection performance varies significantly when dealing with cracks of different shapes and scales. In particular, under complex environmental conditions, detecting fine, irregular, or occluded cracks remains a major challenge. Traditional image-processing-based methods face clear limitations in feature extraction and detection efficiency in practical applications. To address these issues, we propose the CGSW-YOLOv5 algorithm, which enhances detection performance through the following innovations: First, a Concrete Crack Feature Enhancement Block (CNeB) is introduced to improve fine-detail capture. Second, an Adaptive Multi-Scale Feature Aggregation attention mechanism (AMFA) is designed to optimize convolutional kernel selection. Third, the Dynamic Gradient Focusing Weighted IoU loss (DGFW-IoU) is adopted to improve localization accuracy for small targets. Finally, a Lightweight Dual-Stream Convolutional Feature Enhancement module (LDSConv) is constructed to achieve efficient feature utilization. Experimental results show that the CGSW-YOLOv5 algorithm achieves detection accuracies of 71.74% and 72.85% on a self-built dataset and a concrete crack dataset under various environmental conditions (windy, rainy, and foggy), respectively. These results represent improvements of 4.49% and 4.6% over the baseline algorithm, demonstrating superior detection performance and strong environmental adaptability. The proposed method provides an effective solution for intelligent crack detection in concrete structures.