Millimeter-level concrete crack detection network with layer aggregation capability

In the current facility management and maintenance industry, utilizing Unmanned Aerial Vehicles (UAVs) for crack detection holds tremendous potential. However, cracks captured at high altitudes occupy a minimal proportion in images, posing a significant challenge for detection models to effectively extract features. This paper focuses on concrete crack identification and proposes an improved YOLOv5-R network based on the YOLOv5 model and RepNCSPELAN (Re-parameter, CSPNet, ELAN) structure, aiming to enhance the detection accuracy of surface cracks on concrete structures in high-altitude environments. The model primarily optimizes the neck section, where results from multiple convolution modules across channels are fused and embedded into the Cross Stage Partial (CSP) structure, enhancing the contribution of feature information. Additionally, the CSP structure combined with standard convolution forms the generalized efficient layer aggregation network (GELAN) structure, effectively integrating low-dimensional and high-dimensional information and enhancing model gradient flow. The proposed network effectively detects cracks in high-altitude images, providing robust technical support for the infrastructure management and maintenance industry.