Edge–Cloud Intelligence for Sustainable Wind Turbine Blade Transportation: Machine-Vision-Driven Safety Monitoring in Renewable Energy Systems

The transportation of wind turbine blades in remote wind farm areas poses significant safety risks to both personnel and infrastructure. These risks arise from collision hazards, complex terrain, and the difficulty of real-time monitoring under adverse environmental conditions. To address these challenges, this study proposes an intelligent safety monitoring framework that combines machine vision with edge–cloud collaboration. The framework employs an optimized YOLOv7-Tiny model. It is enhanced with convolutional block attention modules (CBAMs) for feature refinement, CARAFE upsampling for better contextual detail, and bidirectional feature pyramid networks (BiFPNs) for multi-scale object detection. The system was validated at the Lingbi Wind Farm in China. It achieved over 95% precision in detecting safety violations, such as unauthorized vehicle intrusions and personnel proximity violations within 2 m, while operating at 48 frames per second. The edge–cloud architecture reduces latency by 30% compared to centralized systems. It enables alert generation within 150 milliseconds. Dynamic risk heatmaps derived from real-time data help reduce collision probability by 42% in high-risk zones. Enhanced spatial resolution further minimizes false alarms in mountainous areas with poor signal conditions. Overall, these improvements reduce operational downtime by 25% and lower maintenance costs by 18% through proactive hazard mitigation. The proposed framework provides a scalable and energy-efficient solution for safety enhancement in renewable energy logistics. It balances computational performance with flexible deployment and addresses key gaps in intelligent monitoring for large-scale wind energy projects. This work offers valuable insights for sustainable infrastructure management.