Investigation of Intelligent Detection Method of Dam Cracks Based on Image Recognition Technology

The development of an intelligent dam crack detection system based on improved YOLOv5 deep learning network represents a significant advancement in water conservancy project safety monitoring. The system utilizes a comprehensive dataset of 4500 crack images, encompassing various crack types and environmental conditions. Through extensive testing and optimization, the system achieves remarkable performance metrics: $93.5 \%$ detection accuracy, $4.2 \%$ leakage rate, and rapid image processing speed of 0.23 seconds per sheet. A rigorous six-month field application at a major hydropower dam demonstrates exceptional system stability of $99.8 \%$ and sophisticated detection capability, successfully identifying cracks as narrow as 0.15 mm. These results validate the system’s practical value for enhancing water conservancy infrastructure safety monitoring and maintenance protocols.