Application of Image Recognition Theory in Concrete Tower Hoisting Construction of Wind Power Projects

With the trend of large-scale wind turbines and the increasing height of tower structures, the concrete-steel hybrid tower (mixed tower) has become a critical solution due to its structural stability and cost-effectiveness. However, challenges such as insufficient precision in prefabricated pipe segments, joint density defects, and verticality deviations during hoisting pose significant safety risks. Traditional manual inspection methods suffer from low efficiency and an inability to monitor complex dynamic conditions in real time. This study proposes an intelligent safety management system based on image recognition theory to address these issues. The system integrates multi-source data acquisition (e.g., drones equipped with visible-light cameras and thermal imaging sensors), edge computing terminals with optimized YOLOv5 models for defect classification (e.g., exposed reinforcement, honeycombing), and 3D reconstruction modules for verticality analysis (accuracy ±2 mm) and joint density assessment. Validated through a case study of a 140m hybrid tower project in Anhui Province, the results demonstrate that the system achieves defect detection accuracy rates exceeding 90.1%, reduces hoisting cycles by 12%, and improves post-correction quality compliance rates to 98.2%. Furthermore, it enhances hazard response efficiency by 66%, significantly mitigating risks such as structural cracks and tower instability. This research provides a robust technical framework for intelligent quality control in wind power projects, aligning with the goals of modern energy systems to enhance renewable infrastructure reliability and grid stability.