On Site Personnel Access Monitoring and Early Warning in Photovoltaic Field Based on AI Image Recognition

The study aims to enhance the management and monitoring of photovoltaic (PV) construction sites, which are often located in remote areas with challenging conditions. Traditional monitoring methods are inefficient, costly, and prone to errors, necessitating the integration of advanced image recognition technology to improve site management. The research introduces image recognition technology, a core aspect of artificial intelligence (AI) and computer vision (CV), to automate the detection and monitoring of equipment, personnel behavior, materials, and construction progress at PV sites. The process involves real-time data collection via high-definition cameras, automated anomaly detection, reporting, and historical data analysis using advanced deep learning models like YOLO. The study developed an efficient and adaptive image recognition model tailored to the complexities of PV construction sites. The model successfully improves the accuracy of detecting safety hazards, optimizes resource utilization, and enhances the overall safety and efficiency of the construction process. The findings highlight the potential of image recognition technology to significantly improve the management of PV construction sites. As the technology advances and becomes more cost-effective, its application is expected to drive higher levels of efficiency, reliability, and safety in the renewable energy sector, supporting the industry's sustainable growth.